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Journal articles on the topic 'Automated language sample analysis'
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Hallett, Terry, and James Steiger. "Automated Analysis of Spoken Language." International Journal for Innovation Education and Research 3, no. 5 (May 31, 2015): 6–13. http://dx.doi.org/10.31686/ijier.vol3.iss5.353.
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We studied the number of words spoken by adult males versus females throughout a six-hour day and during three structured monologues. The six-hour samples were captured and analyzed using an automated speech monitoring and assessment system. The three monologues required different language tasks, and analyses of syntactic and semantic complexity were performed for each. There were no significant gender differences except during a reminiscent monologue when males spoke significantly more words and sentences than females. These results conflict with past research and popular (mis)conceptions.
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Heilmann, John, Alexander Tucci, Elena Plante, and Jon F. Miller. "Assessing Functional Language in School-Aged Children Using Language Sample Analysis." Perspectives of the ASHA Special Interest Groups 5, no. 3 (June 30, 2020): 622–36. http://dx.doi.org/10.1044/2020_persp-19-00079.
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Purpose The goal of this clinical focus article is to illustrate how speech-language pathologists can document the functional language of school-age children using language sample analysis (LSA). Advances in computer hardware and software are detailed making LSA more accessible for clinical use. Method This clinical focus article illustrates how documenting school-age student's communicative functioning is central to comprehensive assessment and how using LSA can meet multiple needs within this assessment. LSA can document students' meaningful participation in their daily life through assessment of their language used during everyday tasks. The many advances in computerized LSA are detailed with a primary focus on the Systematic Analysis of Language Transcripts (Miller & Iglesias, 2019). The LSA process is reviewed detailing the steps necessary for computers to calculate word, morpheme, utterance, and discourse features of functional language. Conclusion These advances in computer technology and software development have made LSA clinically feasible through standardized elicitation and transcription methods that improve accuracy and repeatability. In addition to improved accuracy, validity, and reliability of LSA, databases of typical speakers to document status and automated report writing more than justify the time required. Software now provides many innovations that make LSA simpler and more accessible for clinical use. Supplemental Material https://doi.org/10.23641/asha.12456719
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Fromm, Davida, Saketh Katta, Mason Paccione, Sophia Hecht, Joel Greenhouse, Brian MacWhinney, and Tatiana T. Schnur. "A Comparison of Manual Versus Automated Quantitative Production Analysis of Connected Speech." Journal of Speech, Language, and Hearing Research 64, no. 4 (April 14, 2021): 1271–82. http://dx.doi.org/10.1044/2020_jslhr-20-00561.
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Purpose Analysis of connected speech in the field of adult neurogenic communication disorders is essential for research and clinical purposes, yet time and expertise are often cited as limiting factors. The purpose of this project was to create and evaluate an automated program to score and compute the measures from the Quantitative Production Analysis (QPA), an objective and systematic approach for measuring morphological and structural features of connected speech. Method The QPA was used to analyze transcripts of Cinderella stories from 109 individuals with acute–subacute left hemisphere stroke. Regression slopes and residuals were used to compare the results of manual scoring and automated scoring using the newly developed C-QPA command in CLAN, a set of programs for automatic analysis of language samples. Results The C-QPA command produced two spreadsheet outputs: an analysis spreadsheet with scores for each utterance in the language sample, and a summary spreadsheet with 18 score totals from the analysis spreadsheet and an additional 15 measures derived from those totals. Linear regression analysis revealed that 32 of the 33 measures had good agreement; auxiliary complexity index was the one score that did not have good agreement. Conclusions The C-QPA command can be used to perform automated analyses of language transcripts, saving time and training and providing reliable and valid quantification of connected speech. Transcribing in CHAT, the CLAN editor, also streamlined the process of transcript preparation for QPA and allowed for precise linking of media files to language transcripts for temporal analyses.
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Hsu, Chien-Ju, and Cynthia K. Thompson. "Manual Versus Automated Narrative Analysis of Agrammatic Production Patterns: The Northwestern Narrative Language Analysis and Computerized Language Analysis." Journal of Speech, Language, and Hearing Research 61, no. 2 (February 15, 2018): 373–85. http://dx.doi.org/10.1044/2017_jslhr-l-17-0185.
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Purpose The purpose of this study is to compare the outcomes of the manually coded Northwestern Narrative Language Analysis (NNLA) system, which was developed for characterizing agrammatic production patterns, and the automated Computerized Language Analysis (CLAN) system, which has recently been adopted to analyze speech samples of individuals with aphasia (a) for reliability purposes to ascertain whether they yield similar results and (b) to evaluate CLAN for its ability to automatically identify language variables important for detailing agrammatic production patterns. Method The same set of Cinderella narrative samples from 8 participants with a clinical diagnosis of agrammatic aphasia and 10 cognitively healthy control participants were transcribed and coded using NNLA and CLAN. Both coding systems were utilized to quantify and characterize speech production patterns across several microsyntactic levels: utterance, sentence, lexical, morphological, and verb argument structure levels. Agreement between the 2 coding systems was computed for variables coded by both. Results Comparison of the 2 systems revealed high agreement for most, but not all, lexical-level and morphological-level variables. However, NNLA elucidated utterance-level, sentence-level, and verb argument structure–level impairments, important for assessment and treatment of agrammatism, which are not automatically coded by CLAN. Conclusions CLAN automatically and reliably codes most lexical and morphological variables but does not automatically quantify variables important for detailing production deficits in agrammatic aphasia, although conventions for manually coding some of these variables in Codes for the Human Analysis of Transcripts are possible. Suggestions for combining automated programs and manual coding to capture these variables or revising CLAN to automate coding of these variables are discussed.
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Rysová, Kateřina, Magdaléna Rysová, Michal Novák, Jiří Mírovský, and Eva Hajičová. "EVALD – a Pioneer Application for Automated Essay Scoring in Czech." Prague Bulletin of Mathematical Linguistics 113, no. 1 (October 1, 2019): 9–30. http://dx.doi.org/10.2478/pralin-2019-0004.
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Abstract In the paper, we present EVALD applications (Evaluator of Discourse) for automated essay scoring. EVALD is the first tool of this type for Czech. It evaluates texts written by both native and non-native speakers of Czech. We describe first the history and the present in the automatic essay scoring, which is illustrated by examples of systems for other languages, mainly for English. Then we focus on the methodology of creating the EVALD applications and describe datasets used for testing as well as supervised training that EVALD builds on. Furthermore, we analyze in detail a sample of newly acquired language data – texts written by non-native speakers reaching the threshold level of the Czech language acquisition required e.g. for the permanent residence in the Czech Republic – and we focus on linguistic differences between the available text levels. We present the feature set used by EVALD and – based on the analysis – we extend it with new spelling features. Finally, we evaluate the overall performance of various variants of EVALD and provide the analysis of collected results.
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Egnoto, Michael J., and Darrin J. Griffin. "Analyzing Language in Suicide Notes and Legacy Tokens." Crisis 37, no. 2 (March 2016): 140–47. http://dx.doi.org/10.1027/0227-5910/a000363.
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Abstract. Background: Identifying precursors that will aid in the discovery of individuals who may harm themselves or others has long been a focus of scholarly research. Aim: This work set out to determine if it is possible to use the legacy tokens of active shooters and notes left from individuals who completed suicide to uncover signals that foreshadow their behavior. Method: A total of 25 suicide notes and 21 legacy tokens were compared with a sample of over 20,000 student writings for a preliminary computer-assisted text analysis to determine what differences can be coded with existing computer software to better identify students who may commit self-harm or harm to others. Results: The results support that text analysis techniques with the Linguistic Inquiry and Word Count (LIWC) tool are effective for identifying suicidal or homicidal writings as distinct from each other and from a variety of student writings in an automated fashion. Conclusion: Findings indicate support for automated identification of writings that were associated with harm to self, harm to others, and various other student writing products. This work begins to uncover the viability or larger scale, low cost methods of automatic detection for individuals suffering from harmful ideation.
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Jiao, Yishan, Amy LaCross, Visar Berisha, and Julie Liss. "Objective Intelligibility Assessment by Automated Segmental and Suprasegmental Listening Error Analysis." Journal of Speech, Language, and Hearing Research 62, no. 9 (September 20, 2019): 3359–66. http://dx.doi.org/10.1044/2019_jslhr-s-19-0119.
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Purpose Subjective speech intelligibility assessment is often preferred over more objective approaches that rely on transcript scoring. This is, in part, because of the intensive manual labor associated with extracting objective metrics from transcribed speech. In this study, we propose an automated approach for scoring transcripts that provides a holistic and objective representation of intelligibility degradation stemming from both segmental and suprasegmental contributions, and that corresponds with human perception. Method Phrases produced by 73 speakers with dysarthria were orthographically transcribed by 819 listeners via Mechanical Turk, resulting in 63,840 phrase transcriptions. A protocol was developed to filter the transcripts, which were then automatically analyzed using novel algorithms developed for measuring phoneme and lexical segmentation errors. The results were compared with manual labels on a randomly selected sample set of 40 transcribed phrases to assess validity. A linear regression analysis was conducted to examine how well the automated metrics predict a perceptual rating of severity and word accuracy. Results On the sample set, the automated metrics achieved 0.90 correlation coefficients with manual labels on measuring phoneme errors, and 100% accuracy on identifying and coding lexical segmentation errors. Linear regression models found that the estimated metrics could predict a significant portion of the variance in perceptual severity and word accuracy. Conclusions The results show the promising development of an objective speech intelligibility assessment that identifies intelligibility degradation on multiple levels of analysis.
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Vokes, Martha S., and Anne E. Carpenter. "CellProfiler: Open-Source Software to Automatically Quantify Images." Microscopy Today 16, no. 5 (September 2008): 38–39. http://dx.doi.org/10.1017/s1551929500061757.
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Researchers often examine samples by eye on the microscope — qualitatively scoring each sample for a particular feature of interest. This approach, while suitable for many experiments, sacrifices quantitative results and a permanent record of the experiment. By contrast, if digital images are collected of each sample, software can be used to quantify features of interest. For small experiments, quantitative analysis is often done manually using interactive programs like Adobe Photoshop©. For the large number of images that can be easily collected with automated microscopes, this approach is tedious and time-consuming. NIH Image/ImageJ (http://rsb.info.nih.gov/ij) allows users comfortable writing in a macro language to automate quantitative image analysis. We have developed Cell- Profiler, a free, open-source software package, designed to enable scientists without prior programming experience to quantify relevant features of samples in large numbers of images automatically, in a modular system suitable for processing hundreds of thousands of images.
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Fromm, Davida, Joel Greenhouse, Kaiyue Hou, G. Austin Russell, Xizhen Cai, Margaret Forbes, Audrey Holland, and Brian MacWhinney. "Automated Proposition Density Analysis for Discourse in Aphasia." Journal of Speech, Language, and Hearing Research 59, no. 5 (October 2016): 1123–32. http://dx.doi.org/10.1044/2016_jslhr-l-15-0401.
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Purpose This study evaluates how proposition density can differentiate between persons with aphasia (PWA) and individuals in a control group, as well as among subtypes of aphasia, on the basis of procedural discourse and personal narratives collected from large samples of participants. Method Participants were 195 PWA and 168 individuals in a control group from the AphasiaBank database. PWA represented 6 aphasia types on the basis of the Western Aphasia Battery–Revised (Kertesz, 2006). Narrative samples were stroke stories for PWA and illness or injury stories for individuals in the control group. Procedural samples were from the peanut-butter-and-jelly-sandwich task. Language samples were transcribed using Codes for the Human Analysis of Transcripts (MacWhinney, 2000) and analyzed using Computerized Language Analysis (MacWhinney, 2000), which automatically computes proposition density (PD) using rules developed for automatic PD measurement by the Computerized Propositional Idea Density Rater program (Brown, Snodgrass, & Covington, 2007; Covington, 2007). Results Participants in the control group scored significantly higher than PWA on both tasks. PD scores were significantly different among the aphasia types for both tasks. Pairwise comparisons for both discourse tasks revealed that PD scores for the Broca's group were significantly lower than those for all groups except Transcortical Motor. No significant quadratic or linear association between PD and severity was found. Conclusion Proposition density is differentially sensitive to aphasia type and most clearly differentiates individuals with Broca's aphasia from the other groups.
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Fromm, Davida, Brian MacWhinney, and Cynthia K. Thompson. "Automation of the Northwestern Narrative Language Analysis System." Journal of Speech, Language, and Hearing Research 63, no. 6 (June 22, 2020): 1835–44. http://dx.doi.org/10.1044/2020_jslhr-19-00267.
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Purpose Analysis of spontaneous speech samples is important for determining patterns of language production in people with aphasia. To accomplish this, researchers and clinicians can use either hand coding or computer-automated methods. In a comparison of the two methods using the hand-coding NNLA (Northwestern Narrative Language Analysis) and automatic transcript analysis by CLAN (Computerized Language Analysis), Hsu and Thompson (2018) found good agreement for 32 of 51 linguistic variables. The comparison showed little difference between the two methods for coding most general (i.e., utterance length, rate of speech production), lexical, and morphological measures. However, the NNLA system coded grammatical measures (i.e., sentence and verb argument structure) that CLAN did not. Because of the importance of quantifying these aspects of language, the current study sought to implement a new, single, composite CLAN command for the full set of 51 NNLA codes and to evaluate its reliability for coding aphasic language samples. Method Eighteen manually coded NNLA transcripts from eight people with aphasia and 10 controls were converted into CHAT (Codes for the Human Analysis of Talk) files for compatibility with CLAN commands. Rules from the NNLA manual were translated into programmed rules for CLAN computation of lexical, morphological, utterance-level, sentence-level, and verb argument structure measures. Results The new C-NNLA (CLAN command to compute the full set of NNLA measures) program automatically computes 50 of the 51 NNLA measures and generates the results in a summary spreadsheet. The only measure it does not compute is the number of verb particles. Statistical tests revealed no significant difference between C-NNLA results and those generated by manual coding for 44 of the 50 measures. C-NNLA results were not comparable to manual coding for the six verb argument measures. Conclusion Clinicians and researchers can use the automatic C-NNLA to analyze important variables required for quantification of grammatical deficits in aphasia in a way that is fast, replicable, and accessible without extensive linguistic knowledge and training.
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Lu, Xiaofei. "Automatic analysis of syntactic complexity in second language writing." International Journal of Corpus Linguistics 15, no. 4 (October 29, 2010): 474–96. http://dx.doi.org/10.1075/ijcl.15.4.02lu.
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We describe a computational system for automatic analysis of syntactic complexity in second language writing using fourteen different measures that have been explored or proposed in studies of second language development. The system takes a written language sample as input and produces fourteen indices of syntactic complexity of the sample based on these measures. The system is designed with advanced second language proficiency research in mind, and is therefore developed and evaluated using college-level second language writing data from the Written English Corpus of Chinese Learners (Wen et al. 2005). Experimental results show that the system achieves very high reliability on unseen test data from the corpus. We illustrate how the system is used in an example application to investigate whether and to what extent each of these measures significantly differentiate between different proficiency levels
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Tomas, Ekaterina, and Svetlana Dorofeeva. "Mean Length of Utterance and Other Quantitative Measures of Spontaneous Speech in Russian-Speaking Children." Journal of Speech, Language, and Hearing Research 62, no. 12 (December 18, 2019): 4483–96. http://dx.doi.org/10.1044/2019_jslhr-l-18-0339.
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Purpose This study investigated methodological and theoretical aspects of using mean length of utterance (MLU) and its alternatives in cross-linguistic research and, in particular, its applicability to Russian—a language with a rich system of grammatical and derivational morphemes. Method We collected audio recordings of spontaneous speech samples from 27 Russian-speaking children aged between 2;9 and 5;7 (years;months) over individual play sessions. For each participant, we transcribed the first 100 complete utterances and coded them for several types of utterance length measurements, including length in morphemes (grammatical and derivational), words, and syllables. At a sample level, we calculated the average number of produced unique grammatical forms, getting an alternative quantitative estimate of children's morphosyntactic abilities. Results A combination of Pearson correlation analysis and Bland–Altman difference plots established that MLU can be reliably used in Russian-speaking children aged around 3;0. The average number of unique grammatical forms remains a sensitive measurement of language capabilities even in older children aged over 3;6. Two quantitative measurements, MLU in syllables and morphemes, show good agreement, suggesting that these measurements can be used interchangeably across studies. Sample size analysis revealed that samples under 75 utterances do not provide sufficient reliability for estimating a child's MLU. Conclusions This article demonstrated that MLU can be used in young Russian-speaking children under 3;0–3;6. Also, we showed that the classical morpheme calculation approach can be substituted with counting syllables, which is more time efficient in the absence of automated parsers and is potentially more appropriate for some (e.g., polysynthetic) languages. Our proposed alternative to MLU—the average number of grammatical forms in a sample—appears to be a more sensitive measurement of language capabilities even in older children.
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Rahmati, Omid, Davoud Davoudi Moghaddam, Vahid Moosavi, Zahra Kalantari, Mahmood Samadi, Saro Lee, and Dieu Tien Bui. "An Automated Python Language-Based Tool for Creating Absence Samples in Groundwater Potential Mapping." Remote Sensing 11, no. 11 (June 9, 2019): 1375. http://dx.doi.org/10.3390/rs11111375.
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Although sampling strategy plays an important role in groundwater potential mapping and significantly influences model accuracy, researchers often apply a simple random sampling method to determine absence (non-occurrence) samples. In this study, an automated, user-friendly geographic information system (GIS)-based tool, selection of absence samples (SAS), was developed using the Python programming language. The SAS tool takes into account different geospatial concepts, including nearest neighbor (NN) and hotspot analyses. In a case study, it was successfully applied to the Bojnourd watershed, Iran, together with two machine learning models (random forest (RF) and multivariate adaptive regression splines (MARS)) with GIS and remotely sensed data, to model groundwater potential. Different evaluation criteria (area under the receiver operating characteristic curve (AUC-ROC), true skill statistic (TSS), efficiency (E), false positive rate (FPR), true positive rate (TPR), true negative rate (TNR), and false negative rate (FNR)) were used to scrutinize model performance. Two absence sample types were produced, based on a simple random method and the SAS tool, and used in the models. The results demonstrated that both RF (AUC-ROC = 0.913, TSS = 0.72, E = 0.926) and MARS (AUC-ROC = 0.889, TSS = 0.705, E = 0.90) performed better when using absence samples generated by the SAS tool, indicating that this tool is capable of producing trustworthy absence samples to improve groundwater potential models.
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Reisinger, Debra, Rebecca Shaffer, Ernest Pedapati, Kelli Dominick, and Craig Erickson. "A Pilot Quantitative Evaluation of Early Life Language Development in Fragile X Syndrome." Brain Sciences 9, no. 2 (January 29, 2019): 27. http://dx.doi.org/10.3390/brainsci9020027.
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Language delay and communication deficits are a core characteristic of the fragile X syndrome (FXS) phenotype. To date, the literature examining early language development in FXS is limited potentially due to barriers in language assessment in very young children. The present study is one of the first to examine early language development through vocal production and the language learning environment in infants and toddlers with FXS utilizing an automated vocal analysis system. Child vocalizations, conversational turns, and adult word counts in the home environment were collected and analyzed in a group of nine infants and toddlers with FXS and compared to a typically developing (TD) normative sample. Results suggest infants and toddlers with FXS are exhibiting deficits in their early language skills when compared to their chronological expectations. Despite this, when accounting for overall developmental level, their early language skills appear to be on track. Additionally, FXS caregivers utilize less vocalizations around infants and toddlers with FXS; however, additional research is needed to understand the true gap between FXS caregivers and TD caregivers. These findings provide preliminary information about the early language learning environment and support for the feasibility of utilizing an automated vocal analysis system within the FXS population that could ease data collection and further our understanding of the emergence of language development.
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Gilkerson, Jill, Yiwen Zhang, Dongxin Xu, Jeffrey A. Richards, Xiaojuan Xu, Fan Jiang, James Harnsberger, and Keith Topping. "Evaluating Language Environment Analysis System Performance for Chinese: A Pilot Study in Shanghai." Journal of Speech, Language, and Hearing Research 58, no. 2 (April 2015): 445–52. http://dx.doi.org/10.1044/2015_jslhr-l-14-0014.
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Purpose The purpose of this study was to evaluate performance of the Language Environment Analysis (LENA) automated language-analysis system for the Chinese Shanghai dialect and Mandarin (SDM) languages. Method Volunteer parents of 22 children aged 3–23 months were recruited in Shanghai. Families provided daylong in-home audio recordings using LENA. A native speaker listened to 15 min of randomly selected audio samples per family to label speaker regions and provide Chinese character and SDM word counts for adult speakers. LENA segment labeling and counts were compared with rater-based values. Results LENA demonstrated good sensitivity in identifying adult and child; this sensitivity was comparable to that of American English validation samples. Precision was strong for adults but less so for children. LENA adult word count correlated strongly with both Chinese characters and SDM word counts. LENA conversational turn counts correlated similarly with rater-based counts after the exclusion of three unusual samples. Performance related to some degree to child age. Conclusions LENA adult word count and conversational turn provided reasonably accurate estimates for SDM over the age range tested. Theoretical and practical considerations regarding LENA performance in non-English languages are discussed. Despite the pilot nature and other limitations of the study, results are promising for broader cross-linguistic applications.
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Mahurin-Smith, Jamie, Monique T. Mills, and Rong Chang. "Rare Vocabulary Production in School-Age Narrators From Low-Income Communities." Language, Speech, and Hearing Services in Schools 52, no. 1 (January 18, 2021): 51–63. http://dx.doi.org/10.1044/2020_lshss-19-00120.
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Purpose This study was designed to assess the utility of a tool for automated analysis of rare vocabulary use in the spoken narratives of a group of school-age children from low-income communities. Method We evaluated personal and fictional narratives from 76 school-age children from low-income communities ( M age = 9;3 [years;months]). We analyzed children's use of rare vocabulary in their narratives, with the goal of evaluating relationships among rare vocabulary use, performance on standardized language tests, language sample measures, sex, and use of African American English. Results Use of rare vocabulary in school-age children is robustly correlated with established language sample measures. Male sex was also significantly associated with more frequent rare vocabulary use. There was no association between rare vocabulary use and use of African American English. Discussion Evaluation of rare vocabulary use in school-age children may be a culturally fair assessment strategy that aligns well with existing language sample measures.
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Moxon, Simon. "Exploring the Effects of Automated Pronunciation Evaluation on L2 Students in Thailand." IAFOR Journal of Education 9, no. 3 (June 11, 2021): 41–57. http://dx.doi.org/10.22492/ije.9.3.03.
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A significant barrier to effective communication in a second language is the awareness and accurate reproduction of phonetic sounds absent in the mother tongue. This study investigated whether the automated evaluation of phonetic accuracy using speech recognition technology could improve the pronunciation skills of 105 (88 female, 17 male) Thai undergraduate students studying English in Thailand. A pre-test, post-test design was employed using treatment and control sample groups, reversed over two six-week periods. Treatment group students were given access to an online platform on which they could record and submit their speech for automated evaluation and feedback via SpeechAce, a speech recognition interface designed to evaluate pronunciation and fluency. Independent samples t-test analysis of the results showed statistically significant improvement in pronunciation accuracy of students in the treatment group when compared to those in the control group (t (89) = 2.086, p = .040, 95% CI [.083, 3.423]), (t (89) = -4.692, p < .001, 95% CI [-5.157, -2.089]). Pearson’s correlation analysis indicated a weak to moderate, but statistically significant correlation between frequency of practise and pronunciation test score (r =.508, p < .001), (r = .384, p = .021). The study has limitations as the sample group was predominantly female, and time constraints limited students’ use of the software. Future studies should investigate possible gender differences and experiment with different forms of visual feedback.
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Baxter, Tatiana, Hyeon-Seung Lee, Lénie Torregrossa, Seoyeon Kim, and Sohee Park. "M227. AN EXPLORATORY STUDY OF AUTOMATED LINGUISTIC ANALYSIS OF SOCIAL NARRATIVES IN SCHIZOPHRENIA." Schizophrenia Bulletin 46, Supplement_1 (April 2020): S222—S223. http://dx.doi.org/10.1093/schbul/sbaa030.539.
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Abstract Background Schizophrenia has been suggested to be a disorder of social communication, which depends on the way language is used to convey thoughts, beliefs, feelings, and intentions. Everyday language can also reveal personality, emotions, and social skills of the speaker. Extensive past research affirms the central importance of language and thought disorder as diagnostic features of schizophrenia, mostly focused on the neurocognitive aspects of language output collected during clinical interviews, and not on the social nature of language. In this study, we examined narratives written in response to viewing social scenes by individuals with schizophrenia (SZ) and matched controls (CO) using an automated computational linguistics and statistical-based text analysis tool that computes socially-relevant variables. Methods 23 individuals with schizophrenia (SZ) and 23 demographically matched controls (CO) were shown paintings of social situations, and were asked to write reflections describing what they thought and felt about these scenes. Two pictures were presented consecutively. There was no time limit. Resulting narratives were analyzed with the Linguistic Inquiry and Word Count program (LIWC; Pennebaker et al, 2015). LIWC computes basic linguistic variables such as the % of self-referring and non-self-referring pronouns, social and emotion words, and cognitive items. LIWC also generates 4 complex variables: formal and logical thinking patterns (“analytic”); social status or confidence (“clout”); authenticity, and emotional tone. Clinical symptoms in SZ were assessed using the Scale for the Assessment of Positive Symptoms (SAPS) and the Scale for the Assessment of Negative Symptoms (SANS). For all participants, the UCLA Loneliness Scale, the National Adult Reading Test (NART), and the Edinburgh Handedness Inventory (EHI) were administered. Results The two groups did not differ in NART or EHI. There was no group difference in the number of words produced. SZ produced greater number of pronouns than CO but this effect was driven by a higher % of self-referring pronouns in SZ than CO, with no group difference in non self-referring pronouns. For complex variables, CO scored significantly higher in authenticity than SZ but no group differences were observed in clout, analytics, or emotional tone. Loneliness was higher in SZ compared with CO. There were no other significant group differences. In SZ, pronoun use was correlated with positive symptoms (especially with ratings of hallucinations, bizarre behavior, delusions of mind-reading and thought broadcasting) and inversely correlated with negative symptoms (avolition, apathy and motor retardation). Social words were inversely correlated with SAPS Thought Disorder. Clout was inversely correlated with SANS Alogia and SAPS Thought Disorder. Authenticity was correlated with SANS Anhedonia and Asociality. In CO, loneliness was correlated with the % negative emotion words and NART was correlated with total number of emotion words. Discussion We used an automated linguistic analysis tool to extract information relevant to social communication from written narratives. We found group differences in the use of pronouns and authenticity. We also observed associations of clinical symptoms with certain social aspects of language use in schizophrenia. One advantage of automated text analysis tools is the minimization of implicit biases inherent in ratings of interviews. Limitations of this study include lack of direct social functioning measures and the sample size. Future work will incorporate linguistic text analysis within a social paradigm to directly examine the role of language use in social functioning.
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Vitkus, Donatas, Žilvinas Steckevičius, Nikolaj Goranin, Diana Kalibatienė, and Antanas Čenys. "Automated Expert System Knowledge Base Development Method for Information Security Risk Analysis." International Journal of Computers Communications & Control 14, no. 6 (November 27, 2019): 743–58. http://dx.doi.org/10.15837/ijccc.2019.6.3668.
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Information security risk analysis is a compulsory requirement both from the side of regulating documents and information security management decision making process. Some researchers propose using expert systems (ES) for process automation, but this approach requires the creation of a high-quality knowledge base. A knowledge base can be formed both from expert knowledge or information collected from other sources of information. The problem of such approach is that experts or good quality knowledge sources are expensive. In this paper we propose the problem solution by providing an automated ES knowledge base development method. The method proposed is novel since unlike other methods it does not integrate ontology directly but utilizes automated transformation of existing information security ontology elements into ES rules: The Web Ontology Rule Language (OWL RL) subset of ontology is segregated into Resource Description Framework (RDF) triplets, that are transformed into Rule Interchange Format (RIF); RIF rules are converted into Java Expert System Shell (JESS) knowledge base rules. The experiments performed have shown the principal method applicability. The created knowledge base was later verified by performing comparative risk analysis in a sample company.
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Smith, Michael, Kevin T. Cunningham, and Katarina L. Haley. "Automating Error Frequency Analysis via the Phonemic Edit Distance Ratio." Journal of Speech, Language, and Hearing Research 62, no. 6 (June 19, 2019): 1719–23. http://dx.doi.org/10.1044/2019_jslhr-s-18-0423.
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Purpose Many communication disorders result in speech sound errors that listeners perceive as phonemic errors. Unfortunately, manual methods for calculating phonemic error frequency are prohibitively time consuming to use in large-scale research and busy clinical settings. The purpose of this study was to validate an automated analysis based on a string metric—the unweighted Levenshtein edit distance—to express phonemic error frequency after left hemisphere stroke. Method Audio-recorded speech samples from 65 speakers who repeated single words after a clinician were transcribed phonetically. By comparing these transcriptions to the target, we calculated the percent segments with a combination of phonemic substitutions, additions, and omissions and derived the phonemic edit distance ratio, which theoretically corresponds to percent segments with these phonemic errors. Results Convergent validity between the manually calculated error frequency and the automated edit distance ratio was excellent, as demonstrated by nearly perfect correlations and negligible mean differences. The results were replicated across 2 speech samples and 2 computation applications. Conclusions The phonemic edit distance ratio is well suited to estimate phonemic error rate and proves itself for immediate application to research and clinical practice. It can be calculated from any paired strings of transcription symbols and requires no additional steps, algorithms, or judgment concerning alignment between target and production. We recommend it as a valid, simple, and efficient substitute for manual calculation of error frequency.
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Fetterman, Adam K., Nicholas D. Evans, Julie J. Exline, and Brian P. Meier. "What shall we call God? An exploration of metaphors coded from descriptions of God from a large U.S. undergraduate sample." PLOS ONE 16, no. 7 (July 12, 2021): e0254626. http://dx.doi.org/10.1371/journal.pone.0254626.
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People use numerous metaphors to describe God. God is seen as a bearded man, light, and love. Based on metaphor theories, the metaphors people use to refer to God reflect how people think about God and could, in turn, reflect their worldview. However, little work has explored the common metaphors for God. This was the purpose of the current investigation. Four trained raters coded open-ended responses from predominantly Christian U.S. undergraduates (N = 2,923) describing God for the presence or absence of numerous metaphoric categories. We then assessed the frequency of each of the metaphor categories. We identified 16 metaphor categories that were present in more than 1% of the responses. The top categories were “GOD IS POWER,” “GOD IS HUMAN,” and “GOD IS MALE.” These findings were similar across religious affiliations. We attempted to support our coding analysis using top-down and bottom-up automated language analysis. Results from these analyses provided added confidence to our conclusions. We discuss the implications of our findings and the potential for future studies investigating important psychological and behavioral outcomes of using different metaphors for God.
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Kashinath, Shubha, Aubrey Pearman, and Andrea Canales. "Using Technology to Facilitate Authentic Assessment of Bilingual Preschool Children." Perspectives on Communication Disorders and Sciences in Culturally and Linguistically Diverse (CLD) Populations 22, no. 1 (April 2015): 15–24. http://dx.doi.org/10.1044/cds22.1.15.
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An increasing number of children in preschools and schools around the country are from culturally and linguistically diverse backgrounds and speak multiple languages. However, clinicians are often challenged in conducting least-biased assessments of bilingual children, which often results in over-referral or under- referral of these children to special education and related services. Utilizing naturalistic and authentic assessment of child language such as language sampling is a recommended approach to augment traditional assessments in clinical settings. The Language ENvironment Analysis (LENA) technology offers clinicians a time-and cost effective means to gathering representative language samples across home and school environments to help determine the presence of speech-language impairment in young bilingual children. We describe an exploratory study using the LENA with five Spanish-English bilingual children to identify the accuracy of traditionally transcribed child word counts as compared to the automated child vocalization analyses obtained through the LENA. Results indicate the need for more research to fully explore the clinical utility of this technology for assessment of bilingual children.
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Larson, Anne L., Tyson S. Barrett, and Scott R. McConnell. "Exploring Early Childhood Language Environments: A Comparison of Language Use, Exposure, and Interactions in the Home and Childcare Settings." Language, Speech, and Hearing Services in Schools 51, no. 3 (July 15, 2020): 706–19. http://dx.doi.org/10.1044/2019_lshss-19-00066.
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Purpose This study was conducted in a large Midwestern metropolitan area to examine the language environments at home and in center-based childcare for young children who are living in poverty. We compared child language use and exposure in the home and childcare settings using extended observations with automated Language Environment Analysis to gain a deeper understanding of the environmental factors that may affect change in language outcomes for young children. Method Thirty-eight children, along with parents ( n = 38) and childcare providers ( n = 14) across five childcare centers, participated in this study. Each child completed a standardized language assessment and two daylong recordings with Language Environment Analysis to determine the number of adult words, conversational turns, and child vocalizations that occurred in each setting. Data were analyzed at 5-min intervals across each recording. Results Comparisons between home recordings in this sample and a comparison group showed reliably higher rates of adult words and conversational turns in the home setting. Linear mixed-effects regression models showed significant differences in the child language environments, with the home setting providing higher levels of language input and use. These effects were still meaningful after accounting for the time of day, participant demographic characteristics, and child language ability. Conclusions Practical implications for supporting child language development across settings are discussed, and suggestions for further research are provided. Supplemental Material https://doi.org/10.23641/asha.12042678
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Suting, Louisa B., and Jennifer Mozeiko. "Analysis of Real-World Language Use in a Person With Wernicke's Aphasia." Perspectives of the ASHA Special Interest Groups 6, no. 3 (June 25, 2021): 553–65. http://dx.doi.org/10.1044/2021_persp-20-00178.
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Purpose In this study, we evaluate the use of a technology called the Language ENvironment Analysis (LENA) Pro System to quantify the language of a participant with severe Wernicke's aphasia in their home environment. We aimed to characterize language use at home, particularly as it changed in response to an intensive aphasia treatment. Method The participant was trained to use a wearable recording device pre and post 30 hr of intensive aphasia treatment. LENA software was used to process the language data and to determine word counts and conversational turns and compared to manual analysis. Various communication variables were coded for all conversation samples. Results The participant operated the device independently and provided 30 hr of recordings for analysis. Posttreatment, the participant demonstrated a 78.4% increase in adult word count, a 27.5% increase in conversational turn count, an increase in the number of communication partners, and in the diversity of communication environments. There was a 26% decrease in the amount of time spent on electronics and a 140% increase in the number of instances conversing in a social setting. Manual and automated measures showed poor agreement for this particular participant. Conclusions In this study, we establish the feasibility of using LENA to collect language samples in a participant with severe Wernicke's aphasia in their home environment. Using this method, we were able to characterize and quantify language samples in multiple dimensions before and after language treatment.
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Reggiannini, Brian, Stephen J. Sheinkopf, Harvey F. Silverman, Xiaoxue Li, and Barry M. Lester. "A Flexible Analysis Tool for the Quantitative Acoustic Assessment of Infant Cry." Journal of Speech, Language, and Hearing Research 56, no. 5 (October 2013): 1416–28. http://dx.doi.org/10.1044/1092-4388(2013/11-0298).
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Purpose In this article, the authors describe and validate the performance of a modern acoustic analyzer specifically designed for infant cry analysis. Method Utilizing known algorithms, the authors developed a method to extract acoustic parameters describing infant cries from standard digital audio files. They used a frame rate of 25 ms with a frame advance of 12.5 ms. Cepstral-based acoustic analysis proceeded in 2 phases, computing frame-level data and then organizing and summarizing this information within cry utterances. Using signal detection methods, the authors evaluated the accuracy of the automated system to determine voicing and to detect fundamental frequency (F 0 ) as compared to voiced segments and pitch periods manually coded from spectrogram displays. Results The system detected F 0 with 88% to 95% accuracy, depending on tolerances set at 10 to 20 Hz. Receiver operating characteristic analyses demonstrated very high accuracy at detecting voicing characteristics in the cry samples. Conclusions This article describes an automated infant cry analyzer with high accuracy to detect important acoustic features of cry. A unique and important aspect of this work is the rigorous testing of the system's accuracy as compared to ground-truth manual coding. The resulting system has implications for basic and applied research on infant cry development.
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Dzienisiewicz, Daniel, and Yury Fedorushkov. "Automatyzacja wizualizacji grafowej synonimów dla potrzeb dydaktyki języków obcych (na przykładzie przymiotników rosyjskich z prefiksem „без-”/„бес-”)." Kultury Wschodniosłowiańskie - Oblicza i Dialog, no. 4 (September 22, 2018): 33–48. http://dx.doi.org/10.14746/kw.2014.4.3.
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The article presents methods for application of lexicographic data for the purposes of foreign language teaching and linguistic studies. The automated graph visualization technique is introduced. Both the retrieval and the visualization of linguistic data in form of directed graphs have been conducted with the use of available computer software. The material on which the above-mentioned operations have been performed comprises the group of Russian adjectives with без- / бес- prefixes and the synonymic chains initiated by them. The automated extraction with the use of regular expressions, the steps leading to the edition of the generated database and the import of the final data base into a visualization software are described in the article. The grammatical and semantic characteristics of the sample group of lexemes are not the focus of this article, although further analysis of the selected vocabulary units is planned to be conducted in future studies.
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Vessia, G., M. Parise, M. T. Brunetti, S. Peruccacci, M. Rossi, C. Vennari, and F. Guzzetti. "Automated reconstruction of rainfall events responsible for shallow landslides." Natural Hazards and Earth System Sciences 14, no. 9 (September 10, 2014): 2399–408. http://dx.doi.org/10.5194/nhess-14-2399-2014.
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Abstract. Over the last 40 years, many contributions have identified empirical rainfall thresholds (e.g. rainfall intensity (I) vs. rainfall duration (D), cumulated rainfall vs. rainfall duration (ED), cumulated rainfall vs. rainfall intensity (EI)) for the possible initiation of shallow landslides, based on local and global inventories. Although different methods to trace the threshold curves have been proposed and discussed in literature, a systematic study to develop an automated procedure to select the rainfall event responsible for the landslide occurrence has only rarely been addressed. Objective criteria for estimating the rainfall responsible for the landslide occurrence play a prominent role on the threshold values. In this paper, two criteria for the identification of the effective rainfall events are presented. The first criterion is based on the analysis of the time series of rainfall mean intensity values over 1 month preceding the landslide occurrence. The second criterion is based on the analysis of the trend in the time function of the cumulated mean intensity series calculated from the rainfall records measured through rain gauges. The two criteria have been implemented in an automated procedure that is written in the R language. A sample of 100 shallow landslides collected in Italy from 2002 to 2012 was used to calibrate the procedure. The cumulated event rainfall (E) and duration (D) of rainfall events that triggered the documented landslides are calculated through the new procedure and are fitted with power law in the D, E diagram. The results are discussed by comparing the D, E pairs calculated by the automated procedure and the ones by the expert method.
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Cho, Sunghye, Naomi Nevler, Sanjana Shellikeri, Natalia Parjane, David J. Irwin, Neville Ryant, Sharon Ash, Christopher Cieri, Mark Liberman, and Murray Grossman. "Lexical and Acoustic Characteristics of Young and Older Healthy Adults." Journal of Speech, Language, and Hearing Research 64, no. 2 (February 17, 2021): 302–14. http://dx.doi.org/10.1044/2020_jslhr-19-00384.
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Purpose This study examines the effect of age on language use with an automated analysis of digitized speech obtained from semistructured, narrative speech samples. Method We examined the Cookie Theft picture descriptions produced by 37 older and 76 young healthy participants. Using modern natural language processing and automatic speech recognition tools, we automatically annotated part-of-speech categories of all tokens, calculated the number of tense-inflected verbs, mean length of clause, and vocabulary diversity, and we rated nouns and verbs for five lexical features: word frequency, familiarity, concreteness, age of acquisition, and semantic ambiguity. We also segmented the speech signals into speech and silence and calculated acoustic features, such as total speech time, mean speech and pause segment durations, and pitch values. Results Older speakers produced significantly more fillers, pronouns, and verbs and fewer conjunctions, determiners, nouns, and prepositions than young participants. Older speakers' nouns and verbs were more familiar, more frequent (verbs only), and less ambiguous compared to those of young speakers. Older speakers produced shorter clauses with a lower vocabulary diversity than young participants. They also produced shorter speech segments and longer pauses with increased total speech time and total number of words. Lastly, we observed an interaction of age and sex in pitch ranges. Conclusions Our results suggest that older speakers' lexical content is less diverse, and these speakers produce shorter clauses than young participants in monologic, narrative speech. Our findings show that lexical and acoustic characteristics of semistructured speech samples can be examined with automated methods.
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Bjekic, Jovana, Ljiljana Lazarevic, Marko Zivanovic, and Goran Knezevic. "Psychometric evaluation of the Serbian dictionary for automatic text analysis - LIWCser." Psihologija 47, no. 1 (2014): 5–32. http://dx.doi.org/10.2298/psi1401005b.
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LIWC (Linguistic Inquiry and Word Count) is widely used word-level content analysis software. It was used in large number of studies in the fields of clinical, social and personality psychology, and it is adapted for text analysis in 11 world languages. The aim of this research was to validate empirically newly constructed adaptation of LIWC software for Serbian language (LIWCser). The sample of the texts consisted of 384 texts in Serbian and 141 texts in English. It included scientific paper abstracts, newspaper articles, movie subtitles, short stories and essays. Comparative analysis of Serbian and English version of the software demonstrated acceptable level of equivalence (ICCM=.70). Average coverage of the texts with LIWCser dictionary was 69.93%, and variability of this measure in different types of texts is in line with expected. Adaptation of LIWC software for Serbian opens entirely new possibilities of assessment of spontaneous verbal behaviour that is highly relevant for different fields of psychology.
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VanDam, Mark, and Christine Yoshinaga-Itano. "Use of the LENA Autism Screen with Children who are Deaf or Hard of Hearing." Medicina 55, no. 8 (August 16, 2019): 495. http://dx.doi.org/10.3390/medicina55080495.
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Background and Objectives: This systematic review reports the evidence from the literature concerning the potential for using an automated vocal analysis, the Language ENvironment Analysis (LENA, LENA Research Foundation, Boulder, CO, USA) in the screening process for children at risk for autism spectrum disorder (ASD) and deaf or hard of hearing (D/HH). ASD and D/HH have increased comorbidity, but current behavioral diagnostic and screening tools have limitations. The LENA Language Autism Screen (LLAS) may offer an additional tool to disambiguate ASD from D/HH in young children. Materials and Methods: We examine empirical reports that use automatic vocal analysis methods to differentiate disordered from typically developing children. Results: Consensus across the sampled scientific literature shows support for use of automatic methods for screening and disambiguation of children with ASD and D/HH. There is some evidence of vocal differentiation between ASD, D/HH, and typically-developing children warranting use of the LLAS, but additional empirical evidence is needed to better understand the strengths and weaknesses of the tool. Conclusions: The findings reported here warrant further, more substantive, methodologically-sound research that is fully powered to show a reliable difference. Findings may be useful for both clinicians and researchers in better identification and understanding of communication disorders.
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Vessia, G., M. Parise, M. T. Brunetti, S. Peruccacci, M. Rossi, C. Vennari, and F. Guzzetti. "Automated reconstruction of rainfall events responsible for shallow landslides." Natural Hazards and Earth System Sciences Discussions 2, no. 4 (April 25, 2014): 2869–90. http://dx.doi.org/10.5194/nhessd-2-2869-2014.
Full textAbstract:
Abstract. Over the last 40 years, many contributions have been devoted to identifying the empirical rainfall thresholds (e.g. intensity vs. duration ID, cumulated rainfall vs. duration ED, cumulated rainfall vs. intensity EI) for the initiation of shallow landslides, based on local as well as worldwide inventories. Although different methods to trace the threshold curves have been proposed and discussed in literature, a systematic study to develop an automated procedure to select the rainfall event responsible for the landslide occurrence has rarely been addressed. Nonetheless, objective criteria for estimating the rainfall responsible for the landslide occurrence (effective rainfall) play a prominent role on the threshold values. In this paper, two criteria for the identification of the effective rainfall events are presented: (1) the first is based on the analysis of the time series of rainfall mean intensity values over one month preceding the landslide occurrence, and (2) the second on the analysis of the trend in the time function of the cumulated mean intensity series calculated from the rainfall records measured through rain gauges. The two criteria have been implemented in an automated procedure written in R language. A sample of 100 shallow landslides collected in Italy by the CNR-IRPI research group from 2002 to 2012 has been used to calibrate the proposed procedure. The cumulated rainfall E and duration D of rainfall events that triggered the documented landslides are calculated through the new procedure and are fitted with power law in the (D,E) diagram. The results are discussed by comparing the (D,E) pairs calculated by the automated procedure and the ones by the expert method.
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Källgren, Gunnel. "Automatic Abstracting Content in Text." Nordic Journal of Linguistics 11, no. 1-2 (June 1988): 89–110. http://dx.doi.org/10.1017/s0332586500001761.
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The article deals with the possibility of constructing a synopsis of a given text by automatic means from the text itself. It is suggested that this might be done on the basis of a statistical analysis of the vocabulary and collocations in the vocabulary, followed by simple parsing of selected parts of the text, and simple textual analysis, everything based on the results of the vocabulary analysis. The sentences and sentence fragments that are found to be central can then be used for the reconstruction of a substantially abridged version of the original text. After a discussion of the background of the model, a sample analysis is shown.
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Demjén, Zsófia. "Motion and conflicted self metaphors in Sylvia Plath’s ‘Smith Journal’." Metaphor and the Social World 1, no. 1 (July 22, 2011): 7–25. http://dx.doi.org/10.1075/msw.1.1.02dem.
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This paper considers how mental states can be conveyed by metaphorical expressions in texts of a personal nature. Figurative language is understood to play an important role in the expression of such complex nuanced phenomena (Lakoff & Johnson, 1999; Kövecses, 2000; Gibbs, Leggit & Turner, 2002). This study focuses on two main groups of metaphors, linked to mental states, in the Smith Journal of “The Unabridged Journals of Sylvia Plath”. ‘Mental state’ here refers to various aspects of cognitive functioning, but with a focus on mental states of affect — mental states that are intrinsically valenced (Ortony & Turner, 1990). Specifically, this paper focuses on metaphors of MOTION and SPLIT SELF. Both manual intensive analyses and automated corpus methodologies are employed in the investigation: Wmatrix (Rayson, 2009) is used to explore relevant expressions, in order to obtain a comprehensive understanding of metaphor groups. Relevant expressions are identified by an in-depth manual analysis of sample journal entries. The MIP procedure (Pragglejaz, 2007) is used for metaphor identification, and interpretations draw on research in psychology. Metaphors of mental states are analyzed in terms of their implications for conveying various aspects of mental states, such as valence and intensity.
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Dille, Marilyn F., Garnett P. McMillan, Wendy J. Helt, Dawn Konrad-Martin, and Peter Jacobs. "A Store-and-Forward Tele-Audiology Solution to Promote Efficient Screenings for Ototoxicity during Cisplatin Cancer Treatment." Journal of the American Academy of Audiology 26, no. 09 (October 2015): 750–60. http://dx.doi.org/10.3766/jaaa.15028.
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Background: Tele-audiology improves access, controls cost, and improves efficiency of many aspects within health care. We have developed and validated a device, the ototoxicity identification device (OtoID), which enables remote hearing monitoring by a patient during chemotherapy treatment. Aspects of the design such as patient self-testing and texting of results to the audiology clinic are important features of this device. Purpose: The purpose of this article is to present the efficacy and effectiveness of the OtoID hearing screener. Research Design: A repeated measures design was used in this study. Study Sample: Twenty-one veterans undergoing cisplatin chemotherapy were recruited in this study. Data Collection and Analysis: Participants were tested using the OtoID at each cisplatin treatment by an audiologist using the manual mode of test and the participant using the automated mode of test. Test sensitivity and specificity were developed from the detection (yes/no) of an American Speech-Language-Hearing Association (ASHA) change in hearing. Results: The OtoID had a test sensitivity of 80.6% and specificity of 85.3%. A logistic regression model analysis of the probability of an ASHA shift identified by the automated OtoID was conducted. Separate models were fit to establish effects of age, average baseline thresholds in the sensitive range for ototoxicity (SRO), and dose of cisplatin on the probability of a positive hearing change result. Interactions were also included to evaluate these effects on the sensitivity and false-positive rates of the automated test. Results indicated no statistically significant effects of age, of baseline hearing in the SRO frequencies, or of cisplatin dose. Conclusions: The OtoID automated test can be recommended for use. The automated test provides significant personnel efficiencies. The modem with simple text messaging function recently added to the device improves on these efficiencies.
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Whittington, Craig, Todd Feinman, Sandra Zelman Lewis, Greg Lieberman, and Michael del Aguila. "Clinical practice guidelines: Machine learning and natural language processing for automating the rapid identification and annotation of new evidence." Journal of Clinical Oncology 37, no. 8_suppl (March 10, 2019): 77. http://dx.doi.org/10.1200/jco.2019.37.8_suppl.77.
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77 Background: In February 2018, ASCO published a guideline on how clinicians should manage immune-related adverse events (irAEs) in cancer patients treated with immune checkpoint inhibitors (ICPis). Recommendations were based on informal consensus due to a lack of "high-quality" evidence. Our objective was to determine whether DOC Search, a cloud-based AI search engine, could be used to rapidly determine if any new evidence matches the inclusion criteria of the guideline. Methods: PubMed, ASCO abstracts, and 85 RSS feeds were queried within DOC Search to identify publications since the guideline search was last conducted. DOC Search automatically includes comprehensive synonym lists for the search terms entered, and annotates co-occurring characteristics, interventions and outcomes. Results: Between 11/1/2017 and 10/31/2018, 1178 published references were identified (85.7% from PubMed, 13.8% from ASCO, and 0.5% from official RSS feeds). Title/abstract screening of a sample of the most recent articles indicated that 44% were relevant, and of these, 8% specifically reported research on the management of irAEs. Through automated term indexing of search results, some of the most frequently reported terms were melanoma, corticosteroids, and colitis (Table). Conclusions: DOC Search employs robust ontology mapping—including UMLS, ASCO’s proprietary toxonomy, and more—which obviated the need for complex search strings. The machine learning and natural language processing technology provided real-time analysis and automated term indexing of search results, improving our understanding of the rapidly changing evidence landscape. This analysis met the objective to use DOC Search for rapid identification and review of new published evidence for an existing guideline.[Table: see text]
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Stringham, Oliver C., Stephanie Moncayo, Katherine G. W. Hill, Adam Toomes, Lewis Mitchell, Joshua V. Ross, and Phillip Cassey. "Text classification to streamline online wildlife trade analyses." PLOS ONE 16, no. 7 (July 9, 2021): e0254007. http://dx.doi.org/10.1371/journal.pone.0254007.
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Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question ‘how much data is required to have an adequately performing model?’, we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.
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Shah, Romil F., Stefano Bini, and Thomas Vail. "Data for registry and quality review can be retrospectively collected using natural language processing from unstructured charts of arthroplasty patients." Bone & Joint Journal 102-B, no. 7_Supple_B (July 2020): 99–104. http://dx.doi.org/10.1302/0301-620x.102b7.bjj-2019-1574.r1.
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Aims Natural Language Processing (NLP) offers an automated method to extract data from unstructured free text fields for arthroplasty registry participation. Our objective was to investigate how accurately NLP can be used to extract structured clinical data from unstructured clinical notes when compared with manual data extraction. Methods A group of 1,000 randomly selected clinical and hospital notes from eight different surgeons were collected for patients undergoing primary arthroplasty between 2012 and 2018. In all, 19 preoperative, 17 operative, and two postoperative variables of interest were manually extracted from these notes. A NLP algorithm was created to automatically extract these variables from a training sample of these notes, and the algorithm was tested on a random test sample of notes. Performance of the NLP algorithm was measured in Statistical Analysis System (SAS) by calculating the accuracy of the variables collected, the ability of the algorithm to collect the correct information when it was indeed in the note (sensitivity), and the ability of the algorithm to not collect a certain data element when it was not in the note (specificity). Results The NLP algorithm performed well at extracting variables from unstructured data in our random test dataset (accuracy = 96.3%, sensitivity = 95.2%, and specificity = 97.4%). It performed better at extracting data that were in a structured, templated format such as range of movement (ROM) (accuracy = 98%) and implant brand (accuracy = 98%) than data that were entered with variation depending on the author of the note such as the presence of deep-vein thrombosis (DVT) (accuracy = 90%). Conclusion The NLP algorithm used in this study was able to identify a subset of variables from randomly selected unstructured notes in arthroplasty with an accuracy above 90%. For some variables, such as objective exam data, the accuracy was very high. Our findings suggest that automated algorithms using NLP can help orthopaedic practices retrospectively collect information for registries and quality improvement (QI) efforts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):99–104.
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DELANO-WOOD, LISA, MARK W. BONDI, JOSHUA SACCO, NORM ABELES, AMY J. JAK, DAVID J. LIBON, and ANDREA BOZOKI. "Heterogeneity in mild cognitive impairment: Differences in neuropsychological profile and associated white matter lesion pathology." Journal of the International Neuropsychological Society 15, no. 6 (November 2009): 906–14. http://dx.doi.org/10.1017/s1355617709990257.
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AbstractThis study examined whether distinct neuropsychological profiles could be delineated in a sample with Mild Cognitive Impairment (MCI) and whether white matter lesion (WML) burden contributed to MCI group differences. A heterogeneous, clinical sample of 70 older adults diagnosed with MCI was assessed using cognitive scores, and WML was quantified using a semi-automated, volumetric approach on T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Using cluster and discriminant analyses, three distinct groups (Memory/Language, Executive/Processing Speed, and Pure Memory) were empirically derived based on cognitive scores. Results also showed a dose dependent relationship of WML burden to MCI subgroup, with the Executive/Processing Speed subgroup demonstrating significantly higher levels of WML pathology when compared to the other subgroups. In addition, there was a dissociation of lesion type by the two most impaired subgroups (Memory/Language and Executive/Processing Speed) such that the Memory/Language subgroup showed higher periventricular lesion (PVL) and lower deep white matter lesion (DWML) volumes, whereas the Executive/Processing Speed demonstrated higher DWML and lower PVL volumes. Results demonstrate that distinct MCI subgroups can be empirically derived and reliably differentiated from a heterogeneous MCI sample, and that these profiles differ according to WML burden. Overall, findings suggest different underlying pathologies within MCI and contribute to our understanding of MCI subtypes. (JINS, 2009, 15, 906–914.)
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Goulart, Bernardo H. L., Emily Silgard, Christina S. Baik, Aasthaa Bansal, Mikael Anne Greenwood-Hickman, Annika Hanson, Scott David Ramsey, and Stephen Schwartz. "Validation of natural language processing (NLP) for automated ascertainment of EGFR and ALK tests in SEER cases of non-small cell lung cancer (NSCLC)." Journal of Clinical Oncology 35, no. 15_suppl (May 20, 2017): 6528. http://dx.doi.org/10.1200/jco.2017.35.15_suppl.6528.
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6528 Background: The Surveillance, Epidemiology, and End Results (SEER) registries lack information on the Epidermal Growth Factor Receptor (EGFR) mutation and Anaplastic Lymphoma Kinase (ALK) gene rearrangement test results. With the goal of enabling population-based outcomes research in molecularly selected NSCLC subgroups, we conducted a validation study of NLP for ascertainment of EGFR and ALK testing from electronic pathology reports (e-paths) of patients included in the Seattle-Puget Sound (SPS) and Kentucky Cancer (KCR) SEER registries. Methods: We obtained 4,278 and 1,041 e-paths pertaining to 1,634 and 565 patients with stage IV non-squamous NSCLC diagnosed from 1/1/2011 to 12/31/2013 and included in the SPS and KCR registries, respectively. Two oncologists independently reviewed all reports to generate a gold-standard dataset. We used 855 of the SPS reports to train hybrid rule-based and machine learning algorithms for detection of test status (reported vs. not reported), and test result if reported (positive vs negative) for EGFR mutational analysis and ALK testing by FISH, IHC, or gene sequencing. In the remaining 3,423 SPS reports, we conducted a 5-fold cross-validation analysis to estimate the internal NLP sensitivity, specificity, positive predictive value, and negative predictive value for test status and results, respectively. We used a hierarchical rules system to assess the NLP accuracy at the patient level. For external validation, we repeated all analyses in the KCR dataset. Results: In the SPS internal validation report sample, the validity metrics ranged from 97% to 99% for EGFR and ALK test status, and from 95% to 100% for EGFR and ALK test results, respectively. In the KCR external validation report sample, the metrics ranged from 74% to 96% for EGFR and ALK test status, and 2% to 100% for test results, respectively. At the patient level, the NLP accuracy for EGFR and ALK was 95% and 96% (SPS cohort), and 70% and 72% (KCR cohort) respectively. Conclusions: NLP is a valid method for determining EGFR and ALK test status and results for patients included in SEER registries with access to e-path, but the algorithms likely need to be registry-specific.
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Krotova, O. S., I. V. Moskalev, L. A. Khvorova, and O. M. Nazarkina. "Implementation of Effective Models for Classifying Medical Data Using Text Mining." Izvestiya of Altai State University, no. 1(111) (March 6, 2020): 99–104. http://dx.doi.org/10.14258/izvasu(2020)1-16.
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The paper is devoted to the development and implementation of effective models of medical data classification by text mining for decision support in the diagnosis of pulmonological diseases in children and adolescents of the Altai Territory. Medical data contains important information about patients. Test results are usually retained as structured data, but some data are retained in the form of natural language texts (medical history, the results of physical examination, and the results of other examinations). The paper assesses the quality of the developed methods for extracting information from clinical texts. An assessment of the method for the automatic diagnosis of pulmonological diseases in a test sample is conducted. The most informative features, as well as suitable machine learning methods for classifying patients by disease groups, are identified. Many tasks arising in clinical practice can be automated by applying methods for intelligent analysis of structured and unstructured data that will lead to improvement of the healthcare quality. The results of the research indicate the prospect of using models to support decisionmaking in the primary diagnosis of pulmonological diseases in children and adolescents of the Altai Territory.
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Smith, M. A. L., I. Dustin, R. Leathers, and J.-P. Zrÿd. "DEVELOPMENT OF AUTOMATED VISION TECHNIQUES FOR IMMEDIATE ANALYSIS AND CONTROL OF BETALAIN-PRODUCING CELL CULTURES." HortScience 27, no. 6 (June 1992): 572c—572. http://dx.doi.org/10.21273/hortsci.27.6.572c.
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Natural plant pigments (produced as secondary metabolites in cell culture) can replace controversial synthetic chemical colorants to enhance the appearance of processed foods. Intensive bioreactor-based production systems designed for betalain pigment-producing cultures of Beta vulgaris are still not economically competitive, in part due to the slow, prohibitively expensive, and incomplete conventional methods (HPLC analysis, biomass estimates, cell counts) which must be used to assess culture status. As an alternative, software was written using Semper 6 (a high level programming language for image analysis) for collection of exacting morphometric (spatial) and photometric (spectral) process information from an intense violet cell line. Uniform, crisp images of 1 ml culture samples in multiwell plates were captured macroscopically, and the pattern of pigment production was traced at 3 day intervals over the course of a 15 day growth cycle with monochromatic color filters and image grey level data. Rod-shaped cells and aggregates were automatically sorted and measured using parameters of particle size, density, and circularity. The machine vision method offers greater opportunity to fine-tune cell selection for enhanced pigment content.
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Georgiou, Constantinos A., and Michael A. Koupparis. "Construction and evaluation of an automated flow injection-stopped flow analyser for multipoint reaction rate spectrophotometric methods. Determination of ammonia nitrogen, creatinine and phosphate." Journal of Automatic Chemistry 13, no. 5 (1991): 199–207. http://dx.doi.org/10.1155/s1463924691000342.
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The construction and evaluation of a fully automated Flow Injection-Stopped Flow (FI-SF) spectrophotometric analyser is described. A microcomputer (Rockwell AIM 65) is used to control the analyser (sample injection, stop and start of the pump) through a suitable interface. Data acquisition is achieved using a 12 bit ADC card and a suitable subroutine in 6502 assembly language, allowing data sampling at a frequency of 7.5 kHz. The measurement interface and software were evaluated using a voltage ramp generator. A precision of 0.02-1.1% RSD (N =10) was obtained for voltage ramps in the range of 1-37 mVs-1. The FI-SF analyser was evaluated in routine analysis by developing FI-SF kinetic spectrophotometric methods for the determination of ammonia nitrogen (20-250 ppm, 0.4-2.5% RSD) based on the Berthelot reaction, creatinine (20-220 ppm, 0.9-3.6% RSD) based on the Jaffé reaction, and phosphate (5-30 ppm, 1.0-3.3% RSD) based on the phosphomolybdenum blue reaction. The reaction rate is measured by linear fitting of multiple absorbance readings vs time. Algorithms for automated estimation of the residence time, the linear range of the reaction curve, and data treatment are presented.
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Fox, Carly B., Megan Israelsen-Augenstein, Sharad Jones, and Sandra Laing Gillam. "An Evaluation of Expedited Transcription Methods for School-Age Children's Narrative Language: Automatic Speech Recognition and Real-Time Transcription." Journal of Speech, Language, and Hearing Research 64, no. 9 (September 14, 2021): 3533–48. http://dx.doi.org/10.1044/2021_jslhr-21-00096.
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Purpose This study examined the accuracy and potential clinical utility of two expedited transcription methods for narrative language samples elicited from school-age children (7;5–11;10 [years;months]) with developmental language disorder. Transcription methods included real-time transcription produced by speech-language pathologists (SLPs) and trained transcribers (TTs) as well as Google Cloud Speech automatic speech recognition. Method The accuracy of each transcription method was evaluated against a gold-standard reference corpus. Clinical utility was examined by determining the reliability of scores calculated from the transcripts produced by each method on several language sample analysis (LSA) measures. Participants included seven certified SLPs and seven TTs. Each participant was asked to produce a set of six transcripts in real time, out of a total 42 language samples. The same 42 samples were transcribed using Google Cloud Speech. Transcription accuracy was evaluated through word error rate. Reliability of LSA scores was determined using correlation analysis. Results Results indicated that Google Cloud Speech was significantly more accurate than real-time transcription in transcribing narrative samples and was not impacted by speech rate of the narrator. In contrast, SLP and TT transcription accuracy decreased as a function of increasing speech rate. LSA metrics generated from Google Cloud Speech transcripts were also more reliably calculated. Conclusions Automatic speech recognition showed greater accuracy and clinical utility as an expedited transcription method than real-time transcription. Though there is room for improvement in the accuracy of speech recognition for the purpose of clinical transcription, it produced highly reliable scores on several commonly used LSA metrics. Supplemental Material https://doi.org/10.23641/asha.15167355
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Kelly, Sean, Andrew M. Olney, Patrick Donnelly, Martin Nystrand, and Sidney K. D’Mello. "Automatically Measuring Question Authenticity in Real-World Classrooms." Educational Researcher 47, no. 7 (June 29, 2018): 451–64. http://dx.doi.org/10.3102/0013189x18785613.
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Analyzing the quality of classroom talk is central to educational research and improvement efforts. In particular, the presence of authentic teacher questions, where answers are not predetermined by the teacher, helps constitute and serves as a marker of productive classroom discourse. Further, authentic questions can be cultivated to improve teaching effectiveness and consequently student achievement. Unfortunately, current methods to measure question authenticity do not scale because they rely on human observations or coding of teacher discourse. To address this challenge, we set out to use automatic speech recognition, natural language processing, and machine learning to train computers to detect authentic questions in real-world classrooms automatically. Our methods were iteratively refined using classroom audio and human-coded observational data from two sources: (a) a large archival database of text transcripts of 451 observations from 112 classrooms; and (b) a newly collected sample of 132 high-quality audio recordings from 27 classrooms, obtained under technical constraints that anticipate large-scale automated data collection and analysis. Correlations between human-coded and computer-coded authenticity at the classroom level were sufficiently high ( r = .602 for archival transcripts and .687 for audio recordings) to provide a valuable complement to human coding in research efforts.
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Bharoto, Achmad Ramadhani, Nadi Suparno, and Tri Hardi Priyanto. "Automation of Four Circle Diffractomete /Texture Diffractometer for Studies of Crystal Structure and Texture Measurements." Advanced Materials Research 896 (February 2014): 664–67. http://dx.doi.org/10.4028/www.scientific.net/amr.896.664.
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National Nuclear Energy Agency of Indonesia has several neutron beam instruments for materials science research. One of the instrument is Four Circle Diffractometer / Texture Diffractometer for study of crystal structure and texture measurement. Due to the instrument is fully occupied by many samples, the instruments has to be further developed in order to conduct experiment effectively and efficiently. For that reason, the instrument controller has been replaced with a new programmable controller that can handle all axis simultaneously. Since the controller has been replaced with the new one, a software for data acquisition and measurement also has been modified using a visual basic of programming language with an addition of function, i.e. an automatic measurement for either single-crystal or poly-crystal sample. The software calculates four peaks of Miller indices of the sample for determining the crystal position to be scanned. Then, the software performs the automatic measurement started from scanning the axis of θ-2θ, calculating the peak position by fitting the data obtained from sample diffraction data, then positioning the θ and 2θ axis to the peak position. Finally, the software performs the scanning of the π-χ axis for texture analysis, and save the experiment data into a certain format for the texture analysis software, i.e. Material Analysis Using Diffraction (MAUD).
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Lisovets, S., S. Barilko, A. Zenkin, and V. Zdorenko. "Control of Surface Density of Textile Materials Using Automated Scanning System." Metrology and instruments, no. 5 (October 24, 2019): 52–55. http://dx.doi.org/10.33955/2307-2180(5)2019.52-55.
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The paper demonstrates the importance of controlling the surface density of textiles, which include mainly fabrics, knitted fabrics and nonwoven fabrics, in order to improve the quality of their manufacture.
It considers the most accurate methods of controlling surface density by determining the mass to area ratio of the textile sample. And it is also shown that, in addition to high accuracy, such methods have many fundamental disadvantages: the need to obtain a sample of textile material, low productivity, inability to automate the process of determining surface density, and so on.
In addition, it deals with optical methods for controlling surface density based on the imaging of textile material and its subsequent analysis. However, the presence of factors such as entanglement complexity, the presence of pores, and some others does not fully reveal the potential of optical surface density methods.
The paper also shows that at different points in the surface of the textile material, its surface density may differ significantly from its average value. Therefore, there is a need for an automated scanning system that allows radiating and receiving electroacoustic converters to be moved to exactly the point of the surface of the textile material whose surface density requires measurement.
In order to solve the problem, it was proposed to use a toothed belt gear, and to drive it with the help of step motors controlled through drivers. In turn, to communicate drivers with the control computer, it was proposed to use a microcontroller with an integrated USB interface (for example, manufactured by Microchip Technology Inc.), and software for it to write in one of the high-
level programming languages (for example, C #).
This construction of the automated scanning system is due to the fact that the existing means of linear movement, in terms of the design of the scanning system, have a lot of redundancy: too much cost, too much accuracy, the need to use specialized software, and so on.
The use of the proposed linear positioning means will allow the scanning system to have sufficiently high metrological characteristics at a relatively low cost.
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Prud'hommeaux, Emily, and Brian Roark. "Graph-Based Word Alignment for Clinical Language Evaluation." Computational Linguistics 41, no. 4 (December 2015): 549–78. http://dx.doi.org/10.1162/coli_a_00232.
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Among the more recent applications for natural language processing algorithms has been the analysis of spoken language data for diagnostic and remedial purposes, fueled by the demand for simple, objective, and unobtrusive screening tools for neurological disorders such as dementia. The automated analysis of narrative retellings in particular shows potential as a component of such a screening tool since the ability to produce accurate and meaningful narratives is noticeably impaired in individuals with dementia and its frequent precursor, mild cognitive impairment, as well as other neurodegenerative and neurodevelopmental disorders. In this article, we present a method for extracting narrative recall scores automatically and highly accurately from a word-level alignment between a retelling and the source narrative. We propose improvements to existing machine translation–based systems for word alignment, including a novel method of word alignment relying on random walks on a graph that achieves alignment accuracy superior to that of standard expectation maximization–based techniques for word alignment in a fraction of the time required for expectation maximization. In addition, the narrative recall score features extracted from these high-quality word alignments yield diagnostic classification accuracy comparable to that achieved using manually assigned scores and significantly higher than that achieved with summary-level text similarity metrics used in other areas of NLP. These methods can be trivially adapted to spontaneous language samples elicited with non-linguistic stimuli, thereby demonstrating the flexibility and generalizability of these methods.
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Pekunov, Vladimir Viktorovich. "Induction of the transformation rules of the natural language problem statement into a semantic model for generating solver." Программные системы и вычислительные методы, no. 3 (March 2020): 29–39. http://dx.doi.org/10.7256/2454-0714.2020.3.33789.
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The author considers a problem of automatic synthesis (induction) of the rules for transforming the natural language formulation of the problem into a semantic model of the problem. According to this model a program that solves this problem can be generated. The problem is considered in relation to the system of generation, recognition and transformation of programs PGEN ++. Based on the analysis of literary sources, a combined approach was chosen to solve this problem, within which the rules for transforming the natural language formulation into a semantic model of the problem are generated automatically, and the specifications of the generating classes and the rules for generating a program from the model are written manually by a specialist in a specific subject area. Within the framework of object-event models, for the first time, a mechanism for the automatic generation of recognizing scripts and related entities (CSV tables, XPath functions) was proposed. Generation is based on the analysis of the training sample, which includes sentences describing objects in the subject area, in combination with instances of such objects. The analysis is performed by searching for unique keywords and characteristic grammatical relationships, followed by the application of simple eliminative-inducing schemes. A mechanism for the automatic generation of rules for replenishing / completing the primary recognized models to full meaning ones is also proposed. Such generation is performed by analyzing the relations between the objects of the training sample, taking into account information from the specifications of the classes of the subject area. The proposed schemes have been tested on the subject area "Simple vector data processing", the successful transformation of natural language statements (both included in the training set and modified) into semantic models with the subsequent generation of programs solving the assigned tasks is shown.
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Nevler, Naomi, Sharon Ash, Charles Jester, David J. Irwin, Mark Liberman, and Murray Grossman. "Automatic measurement of prosody in behavioral variant FTD." Neurology 89, no. 7 (July 19, 2017): 650–56. http://dx.doi.org/10.1212/wnl.0000000000004236.
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Objective:To help understand speech changes in behavioral variant frontotemporal dementia (bvFTD), we developed and implemented automatic methods of speech analysis for quantification of prosody, and evaluated clinical and anatomical correlations.Methods:We analyzed semi-structured, digitized speech samples from 32 patients with bvFTD (21 male, mean age 63 ± 8.5, mean disease duration 4 ± 3.1 years) and 17 matched healthy controls (HC). We automatically extracted fundamental frequency (f0, the physical property of sound most closely correlating with perceived pitch) and computed pitch range on a logarithmic scale (semitone) that controls for individual and sex differences. We correlated f0 range with neuropsychiatric tests, and related f0 range to gray matter (GM) atrophy using 3T T1 MRI.Results:We found significantly reduced f0 range in patients with bvFTD (mean 4.3 ± 1.8 ST) compared to HC (5.8 ± 2.1 ST; p = 0.03). Regression related reduced f0 range in bvFTD to GM atrophy in bilateral inferior and dorsomedial frontal as well as left anterior cingulate and anterior insular regions.Conclusions:Reduced f0 range reflects impaired prosody in bvFTD. This is associated with neuroanatomic networks implicated in language production and social disorders centered in the frontal lobe. These findings support the feasibility of automated speech analysis in frontotemporal dementia and other disorders.
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Wiesner, Martin, Richard Zowalla, and Monika Pobiruchin. "The Difficulty of German Information Booklets on Psoriasis and Psoriatic Arthritis: Automated Readability and Vocabulary Analysis." JMIR Dermatology 3, no. 1 (February 28, 2020): e16095. http://dx.doi.org/10.2196/16095.
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Background Information-seeking Psoriasis or Psoriatic Arthritis patients are confronted with numerous educational materials when looking through the internet. Literature suggests that only 17.0-21.4% (Psoriasis, Psoriatic Arthritis) of patients have a good level of knowledge about psoriasis treatment and self-management. A study from 1994 found that English Psoriasis/Psoriatic Arthritis brochures required a reading level between grades 8-12 to be understandable, which was confirmed in a follow-up study 20 years later. As readability of written health-related text material should not exceed the sixth-grade level, Psoriasis/Psoriatic Arthritis material seems to be ill-suited to its target audience. However, no data is available on the readability levels of Psoriasis/Psoriatic Arthritis brochures for German-speaking patients, and both the volume and their scope are unclear. Objective This study aimed to analyze freely available educational materials for Psoriasis/Psoriatic Arthritis patients written in German, quantifying their difficulty by assessing both the readability and the vocabulary used in the collected brochures. Methods Data collection was conducted manually via an internet search engine for Psoriasis/Psoriatic Arthritis–specific material, published as PDF documents. Next, raw text was extracted, and a computer-based readability and vocabulary analysis was performed on each brochure. For the readability analysis, we applied the Flesch Reading Ease (FRE) metric adapted for the German language, and the fourth Vienna formula (WSTF). To assess the laymen-friendliness of the vocabulary, the computation of an expert level was conducted using a specifically trained Support Vector Machine classifier. A two-sided, two-sample Wilcoxon test was applied to test whether the difficulty of brochures of pair-wise topic groups was different from each other. Results In total, n=68 brochures were included for readability assessment, of which 71% (48/68) were published by pharmaceutical companies, 22% (15/68) by nonprofit organizations, and 7% (5/68) by public institutions. The collection was separated into four topic groups: basic information on Psoriasis/Psoriatic Arthritis (G1/G2), lifestyle, and behavior with Psoriasis/Psoriatic Arthritis (G3/G4), medication and therapy guidance (G5), and other topics (G6). On average, readability levels were comparatively low, with FRE=31.58 and WSTF=11.84. However, two-thirds of the educational materials (69%; 47/68) achieved a vocabulary score ≦4 (ie, easy, very easy) and were, therefore, suitable for a lay audience. Statistically significant differences between brochure groups G1 and G3 for FRE (P=.001), WSTF (P=.003), and vocabulary measure (L) (P=.01) exist, as do statistically significant differences for G2 and G4 in terms of FRE (P=.03), WSTF (P=.03) and L (P=.03). Conclusions Online Psoriasis/Psoriatic Arthritis patient education materials in German require, on average, a college or university education level. As a result, patients face barriers to understanding the available material, even though the vocabulary used seems appropriate. For this reason, publishers of Psoriasis/Psoriatic Arthritis brochures should carefully revise their educational materials to provide easier and more comprehensible information for patients with lower health literacy levels.