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Al Mahmud, Nahyan, and Shahfida Amjad Munni. "Qualitative Analysis of PLP in LSTM for Bangla Speech Recognition." International journal of Multimedia & Its Applications 12, no. 5 (October 30, 2020): 1–8. http://dx.doi.org/10.5121/ijma.2020.12501.
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The performance of various acoustic feature extraction methods has been compared in this work using Long Short-Term Memory (LSTM) neural network in a Bangla speech recognition system. The acoustic features are a series of vectors that represents the speech signals. They can be classified in either words or sub word units such as phonemes. In this work, at first linear predictive coding (LPC) is used as acoustic vector extraction technique. LPC has been chosen due to its widespread popularity. Then other vector extraction techniques like Mel frequency cepstral coefficients (MFCC) and perceptual linear prediction (PLP) have also been used. These two methods closely resemble the human auditory system. These feature vectors are then trained using the LSTM neural network. Then the obtained models of different phonemes are compared with different statistical tools namely Bhattacharyya Distance and Mahalanobis Distance to investigate the nature of those acoustic features.
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Verduin, Maikel, Sergey Primakov, Inge Compter, Henry C. Woodruff, Sander M. J. van Kuijk, Bram L. T. Ramaekers, Maarten te Dorsthorst, et al. "Prognostic and Predictive Value of Integrated Qualitative and Quantitative Magnetic Resonance Imaging Analysis in Glioblastoma." Cancers 13, no. 4 (February 10, 2021): 722. http://dx.doi.org/10.3390/cancers13040722.
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Glioblastoma (GBM) is the most malignant primary brain tumor for which no curative treatment options exist. Non-invasive qualitative (Visually Accessible Rembrandt Images (VASARI)) and quantitative (radiomics) imaging features to predict prognosis and clinically relevant markers for GBM patients are needed to guide clinicians. A retrospective analysis of GBM patients in two neuro-oncology centers was conducted. The multimodal Cox-regression model to predict overall survival (OS) was developed using clinical features with VASARI and radiomics features in isocitrate dehydrogenase (IDH)-wild type GBM. Predictive models for IDH-mutation, 06-methylguanine-DNA-methyltransferase (MGMT)-methylation and epidermal growth factor receptor (EGFR) amplification using imaging features were developed using machine learning. The performance of the prognostic model improved upon addition of clinical, VASARI and radiomics features, for which the combined model performed best. This could be reproduced after external validation (C-index 0.711 95% CI 0.64–0.78) and used to stratify Kaplan–Meijer curves in two survival groups (p-value < 0.001). The predictive models performed significantly in the external validation for EGFR amplification (area-under-the-curve (AUC) 0.707, 95% CI 0.582–8.25) and MGMT-methylation (AUC 0.667, 95% CI 0.522–0.82) but not for IDH-mutation (AUC 0.695, 95% CI 0.436–0.927). The integrated clinical and imaging prognostic model was shown to be robust and of potential clinical relevance. The prediction of molecular markers showed promising results in the training set but could not be validated after external validation in a clinically relevant manner. Overall, these results show the potential of combining clinical features with imaging features for prognostic and predictive models in GBM, but further optimization and larger prospective studies are warranted.
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Zimmermann, Bettina, Bernice Elger, and David Shaw. "Media Coverage of Ethical Issues in Predictive Genetic Testing: A Qualitative Analysis." AJOB Empirical Bioethics 10, no. 4 (October 2, 2019): 250–64. http://dx.doi.org/10.1080/23294515.2019.1670275.
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Ragin, Charles C. "The Logic of Qualitative Comparative Analysis." International Review of Social History 43, S6 (December 1998): 105–24. http://dx.doi.org/10.1017/s0020859000115111.
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Social scientists often face a fundamental dilemma when they conduct social research. On the one hand, they may emphasize the complexity of social phenomena – a common strategy in ethnographic, historical and macro social research – and offer in–depth case studies sensitive to the specificity of the things they study. On the other hand, they may make broad, homo genizing assumptions about cases, and document generalities – patterns hold across many instances. Research strategies that focus on complexity are often labeled “qualitative”, “case–oriented”, “small–N”, or “intensive”. Those that focus on generality are often labeled “quantitative”, “variable–oriented”, “large–N”, or “extensive”. While the contrasts between these two types social research are substantial, it is easy to exaggerate their differences and t o caricature the two approaches, for example, portraying quantitative work on general patterns as scientific but sterile and oppressive, and qualitative research on small Ns as rich and emancipatory but journalistic. It is important to avoid these caricatures because the contrasts between these two general approaches provide important leads both for finding a middle path between them and for resolving basic methodological issues in social science Social scientists who study cases in an in–depth manner often see empiri cal generalizations simply as a means to another end – the interpretive understanding of cases. In this view, a fundamental goal of social science is t o interpret significant features of the social world and thereby advance our collective understanding of how existing social arrangements came about and why we live the way we do. The rough general patterns that social scientists may be able to identify simply aid the understanding of specific cases; they are not viewed as predictive. Besides, the task of interpreting and then representing socially significant phenomena (or the task of making selected social phenomena significant by representing them) is a much more immediate and tangible goal. In this view, empirical generalizations and social science theory are important – to the extent that they aid the goal interpretive understanding.
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Cao, Mengqiu, Shiteng Suo, Xiao Zhang, Xiaoqing Wang, Jianrong Xu, Wei Yang, and Yan Zhou. "Qualitative and Quantitative MRI Analysis in IDH1 Genotype Prediction of Lower-Grade Gliomas: A Machine Learning Approach." BioMed Research International 2021 (January 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/1235314.
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Purpose. Preoperative prediction of isocitrate dehydrogenase 1 (IDH1) mutation in lower-grade gliomas (LGGs) is crucial for clinical decision-making. This study aimed to examine the predictive value of a machine learning approach using qualitative and quantitative MRI features to identify the IDH1 mutation in LGGs. Materials and Methods. A total of 102 LGG patients were allocated to training ( n = 67 ) and validation ( n = 35 ) cohorts and were subject to Visually Accessible Rembrandt Images (VASARI) feature extraction (23 features) from conventional multimodal MRI and radiomics feature extraction (56 features) from apparent diffusion coefficient maps. Feature selection was conducted using the maximum Relevance Minimum Redundancy method and 0.632+ bootstrap method. A machine learning model to predict IDH1 mutation was then established using a random forest classifier. The predictive performance was evaluated using receiver operating characteristic (ROC) curves. Results. After feature selection, the top 5 VASARI features were enhancement quality, deep white matter invasion, tumor location, proportion of necrosis, and T1/FLAIR ratio, and the top 10 radiomics features included 3 histogram features, 3 gray-level run-length matrix features, and 3 gray-level size zone matrix features and one shape feature. Using the optimal VASARI or radiomics feature sets for IDH1 prediction, the trained model achieved an area under the ROC curve (AUC) of 0.779 ± 0.001 or 0.849 ± 0.008 on the validation cohort, respectively. The fusion model that integrated outputs of both optimal VASARI and radiomics models improved the AUC to 0.879. Conclusion. The proposed machine learning approach using VASARI and radiomics features can predict IDH1 mutation in LGGs.
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Chen, Yi-sheng, Yan-xian Cai, Xue-ran Kang, Zi-hui Zhou, Xin Qi, Chen-ting Ying, Yun-peng Zhang, and Jie Tao. "Predicting the risk of sarcopenia in elderly patients with patellar fracture: development and assessment of a new predictive nomogram." PeerJ 8 (April 15, 2020): e8793. http://dx.doi.org/10.7717/peerj.8793.
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Purpose To develop a risk prediction model for postoperative sarcopenia in elderly patients with patellar fractures in China. Patients and methods We conducted a community survey of patients aged ≥55 years who underwent surgery for patellar fractures between January 2013 and October 2018, through telephone interviews, community visits, and outpatient follow-up. We established a predictive model for assessing the risk of sarcopenia after patellar fractures. We developed the prediction model by combining multivariate logistic regression analysis with the least absolute shrinkage model and selection operator regression (lasso analysis) as well as the Support Vector Machine (SVM) algorithm. The predictive quality and clinical utility of the predictive model were determined using C-index, calibration plots, and decision curve analysis. We also conducted internal sampling methods for qualitative assessment. Result We recruited 137 participants (53 male; mean age, 65.7 years). Various risk factors were assessed, and low body mass index and advanced age were identified as the most important risk factor (P < 0.05). The prediction rate of the model was good (C-index: 0.88; 95% CI [0.80552–0.95448]), with a satisfactory correction effect. The C index is 0.97 in the validation queue and 0.894 in the entire cohort. Decision curve analysis suggested good clinical practicability. Conclusion Our prediction model shows promise as a cost-effective tool for predicting the risk of postoperative sarcopenia in elderly patients based on the following: advanced age, low body mass index, diabetes, less outdoor exercise, no postoperative rehabilitation, different surgical methods, diabetes, open fracture, and removal of internal fixation.
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USMAN, Abdullahi Garba, Selin IŞIK, Sani Isah ABBA, and Filiz MERİÇLİ. "Artificial intelligence–based models for the qualitative and quantitative prediction of a phytochemical compound using HPLC method." TURKISH JOURNAL OF CHEMISTRY 44, no. 5 (October 26, 2020): 1339–51. http://dx.doi.org/10.3906/kim-2003-6.
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Isoquercitrin is a flavonoid chemical compound that can be extracted from different plant species such as Mangifera indica (mango), Rheum nobile, Annona squamosal, Camellia sinensis (tea), and coriander (Coriandrum sativum L.). It possesses various biological activities such as the prevention of thromboembolism and has anticancer, antiinflammatory, and antifatigue activities. Therefore, there is a critical need to elucidate and predict the qualitative and quantitative properties of this phytochemical compound using the high performance liquid chromatography (HPLC) technique. In this paper, three different nonlinear models including artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM),in addition to a classical linear model [multilinear regression analysis (MLR)], were used for the prediction of the retention time (tR) and peak area (PA) for isoquercitrin using HPLC. The simulation uses concentration of the standard, composition of the mobile phases (MP-A and MP-B), and pH as the corresponding input variables. The performance efficiency of the models was evaluated using relative mean square error (RMSE), mean square error (MSE), determination coefficient (DC), and correlation coefficient (CC). The obtained results demonstrated that all four models are capable of predicting the qualitative and quantitative properties of the bioactive compound. A predictive comparison of the models showed that M3 had the highest prediction accuracy among the three models. Further evaluation of the results showed that ANFIS–M3 outperformed the other models and serves as the best model for the prediction of PA. On the other hand, ANN–M3proved its merit and emerged as the best model for tR simulation. The overall predictive accuracy of the best models showed them to be reliable tools for both qualitative and quantitative determination.
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Schmalenbach, M., M. Jungehülsing, P. Theissen, M. Dietlein, U. Schröder, W. Eschner, E. Stennert, H. Schicha, and M. Schmidt. "18F-FDG PET for detecting recurrent head and neck cancer, local lymph node involvement and distant metastases." Nuklearmedizin 43, no. 03 (2004): 91–101. http://dx.doi.org/10.1055/s-0038-1625597.
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Summary Aim: Assessment of the clinical value of 18F-FDG-PET for detection of recurrent head and neck cancer, local lymph node involvement and distant metastases comparing a qualitative visual with a semiquantitative analysis (SUV values). Patients, methods: Retrospective evaluation of 73 18F-FDG PET studies in 55 patients by use of a four-step qualitative visual grading system and calculation of standard uptake values in pathological lesions. Calculation of SUV values in normal regions for generating a map of physiological 18F-FDG distribution. Correlation to histopathological findings and clinical follow-up. Results: 1. Qualitative visual analysis of 18F-FDG PET studies: a) local recurrence sensitivity 79%, specificity 97%, positive predictive value 95%, negative predictive value 85%, and diagnostic accuracy 89%; b) local metastatic lymph nodes 100%, 95%, 85%, 100%, 96%; c) distant metastases 100%, 98%, 86%, 100%, 98%, respectively. 2. Semiquantitative analysis had only little incremental, non-significant value in comparison to qualitative visual analysis for the detection of a local recurrence in two patients: a) local recurrence: sensitivity 83%, specificity 100%, positive predictive value 100%, negative predictive value 88%, and diagnostic accuracy 93%; b) local metastatic lymph nodes or c) distant metastases did not change in comparison to qualitative visual analysis. Conclusion: 18F-FDG PET is an effective tool for re-staging of patients with suspected recurrence after therapy for head and neck cancer.
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Granados Sánchez, Ana María, and Juan Felipe Orejuela Zapata. "Diagnosis of mesial temporal sclerosis: sensitivity, specificity and predictive values of the quantitative analysis of magnetic resonance imaging." Neuroradiology Journal 31, no. 1 (September 13, 2017): 50–59. http://dx.doi.org/10.1177/1971400917731301.
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In the diagnosis of mesial temporal sclerosis (MTS), sensitivity, specificity and predictive values of qualitative assessment using conventional magnetic resonance imaging are low, mainly in mild or bilateral atrophy. Quantitative analysis may improve this performance. We evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of quantitative analysis using the hippocampal volumetric index (HVI) and hippocampal asymmetry index (HAI) compared with qualitative assessment in the MTS diagnosis. Twenty-five patients diagnosed with MTS, and 25 healthy subjects underwent conventional magnetic resonance imaging. Hippocampal volumes were obtained using an automated software (FreeSurfer); HVI and HAI were calculated. Receiver operating characteristic curve analysis was performed to obtain the optimal threshold values. Sensitivity, specificity and predictive values were calculated. Sensitivity, specificity, PPV and NPV for qualitative analysis were 44.00%, 96.00%, 91.67% and 63.16%, respectively. In the quantitative analysis, a threshold value of K = 0.22 for HVI provided a sensitivity value of 76.00%, specificity value of 96.00%, PPV of 95.00% and NPV of 80.00%. A threshold value of K = 0.06 for HAI provided the minimum C1 and C2 errors, with a sensitivity value of 88.00%, specificity value of 100%, PPV of 100% and NPV of 89.30%. A statistically significant difference was observed for HAI ( P < 0.0001), and ipsilateral HVI (left MTS, P = 0.0152; right MTS, P < 0.0001), between MTS and healthy groups. The HVI and HAI, both individually and in conjunction, improved the sensitivity, specificity and predictive values of magnetic resonance imaging in the diagnosis of MTS compared to the qualitative analysis and other quantitative techniques. The HAI is highly accurate in the diagnosis of unilateral MTS, whereas the HVI may be better for bilateral MTS cases.
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Bozzato, Alessandro, Johannes Zenk, Holger Greess, Joachim Hornung, Frank Gottwald, Christina Rabe, and Heinrich Iro. "Potential of ultrasound diagnosis for parotid tumors: Analysis of qualitative and quantitative parameters." Otolaryngology–Head and Neck Surgery 137, no. 4 (October 2007): 642–46. http://dx.doi.org/10.1016/j.otohns.2007.05.062.
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Objective Histology of parotid tumors determines the extent of surgery. The aim was to test ultrasound (US) contrast enhancer-kinetics to identify histologic entities, possibly being superior to qualitative morphological parameters. Study Design In a cross-sectional assessment of ultrasound diagnosis, the subjective US-classification was compared with contrast analysis with histology as gold standard. Subjects and Methods A total of 64 male and 61 female patients with a mean age of 54 years were included, with 13 malignant tumors. These were classified with US morphology, then time-dependent contrast medium analysis. Results A total of 92.8% of tumors were classified correctly as malignant or benign. The sensitivity, specificity, positive- and negative-predictive values were 66.7%, 86.3%, 60.6%, and 89.1% for differentiating Warthin tumors, but only 46.2%, 98.2%, 75%, and 94% for malignant lesions. Contrast parameters yielded significant parameters for benign tumors, not for malignant entities. Conclusion Although contrast medium analysis provided statistical criteria, these, however, do not possess the ability to improve the diagnostic prediction of tumor histology. Neither the morphologic classification nor contrast medium analysis was able to identify a malignant lesion sufficiently.
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Mazurov, B. T. "Geodynamic systems (qualitative research rotational movements)." Geodesy and Cartography 919, no. 1 (February 20, 2017): 35–39. http://dx.doi.org/10.22389/0016-7126-2017-919-1-35-39.
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Geodetic data and their subsequent statistical analysis enable mathematical modeling and identifying the stress-deformed state of geodynamic systems in concern to the aspect of natural and man-made disasters prediction. Geodetic monitoring geodynamic processes is necessary for solving a number of scientific and practical tasks of geodesy i.e. expanding and maintaining the national geodetic network, studying changes in gravity field in time, using GNSS technology. Most important extension of research is mathematical modelling of geodynamic systems in a predictive order. To study the complex (nonlinear) geodynamic processes the appropriate mathematical framework should be selected. Here are theoretical foundations for studying rotation movements of the earth’s surface. A mathematical model of rotary circular structures of the Earth was mentioned. There are mathematical models explaining the nature of sudden global, regional and some local geodynamic processes. They are based on differences in temporal and spatial scales, of geodynamic systems. Theoretical bases of description rotational motions on a plane by a system of differential equations were considered. Some examples of integral curves were given. They can be qualitative characteristics of geodynamic systems. In many cases, a similar trajectory corresponds to the rotational horizontal movements of the earth’s surface.
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Cubitt, Timothy, Ken Wooden, Erin Kruger, and Michael Kennedy. "A predictive model for serious police misconduct by variation of the theory of planned behaviour." Journal of Forensic Practice 22, no. 4 (November 16, 2020): 251–63. http://dx.doi.org/10.1108/jfp-08-2020-0033.
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Purpose Misconduct and deviance amongst police officers are substantial issues in policing around the world. This study aims to propose a prediction model for serious police misconduct by variation of the theory of planned behaviour. Design/methodology/approach Using two data sets, one quantitative and one qualitative, provided by an Australian policing agency, a random forest analysis and a qualitative content analysis was performed. Results were used to inform and extend the framework of the theory of planned behaviour. The traditional and extended theory of planned behaviour models were then tested for predictive utility. Findings Each model demonstrated noteworthy predictive power, however, the extended model performed particularly well. Prior instances of minor misconduct amongst officers appeared important in this rate of prediction, suggesting that remediation of problematic behaviour was a substantial issue amongst misconduct prone officers. Practical implications It is an important implication for policing agencies that prior misconduct was predictive of further misconduct. A robust complaint investigation and remediation process are pivotal to anticipating, remediating and limiting police misconduct, however, early intervention models should not be viewed as the panacea for police misconduct. Originality/value This research constitutes the first behavioural model for police misconduct produced in Australia. This research seeks to contribute to the field of behavioural prediction amongst deviant police officers, and offer an alternative methodology for understanding these behaviours.
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Pulvirenti, Alessandra, Rikiya Yamashita, Jayasree Chakraborty, Natally Horvat, Kenneth Seier, Caitlin A. McIntyre, Sharon A. Lawrence, et al. "Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade." JCO Clinical Cancer Informatics, no. 5 (June 2021): 679–94. http://dx.doi.org/10.1200/cci.20.00121.
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PURPOSE The therapeutic management of pancreatic neuroendocrine tumors (PanNETs) is based on pathological tumor grade assessment. A noninvasive imaging method to grade tumors would facilitate treatment selection. This study evaluated the ability of quantitative image analysis derived from computed tomography (CT) images to predict PanNET grade. METHODS Institutional database was queried for resected PanNET (2000-2017) with a preoperative arterial phase CT scan. Radiomic features were extracted from the primary tumor on the CT scan using quantitative image analysis, and qualitative radiographic descriptors were assessed by two radiologists. Significant features were identified by univariable analysis and used to build multivariable models to predict PanNET grade. RESULTS Overall, 150 patients were included. The performance of models based on qualitative radiographic descriptors varied between the two radiologists (reader 1: sensitivity, 33%; specificity, 66%; negative predictive value [NPV], 63%; and positive predictive value [PPV], 37%; reader 2: sensitivity, 45%; specificity, 70%; NPV, 72%; and PPV, 47%). The model based on radiomics had a better performance predicting the tumor grade with a sensitivity of 54%, a specificity of 80%, an NPV of 81%, and a PPV of 54%. The inclusion of radiomics in the radiographic descriptor models improved both the radiologists' performance. CONCLUSION CT quantitative image analysis of PanNETs helps predict tumor grade from routinely acquired scans and should be investigated in future prospective studies.
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Chen, Jian. "A predictive system for blast furnaces by integrating a neural network with qualitative analysis." Engineering Applications of Artificial Intelligence 14, no. 1 (February 2001): 77–85. http://dx.doi.org/10.1016/s0952-1976(00)00062-2.
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Mandasari, Liannisa, M. R. Nababan, and Djatmika Djatmika. "The Acceptability of Predicting Utterances in Deception Point Novel." International Journal of Multicultural and Multireligious Understanding 6, no. 2 (May 1, 2019): 289. http://dx.doi.org/10.18415/ijmmu.v6i2.649.
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The purpose of this study is to find the type of predicting and the quality of translation on the acceptability aspect. This research can be classified as a descriptive qualitative research with an embeded study and translation of product-oriented. The source of the data in this study is novel in English, entitled Deception Point and its translation in Bahasa. The data were collected through document analysis, questionnaire and focus group discussion. The data was sentence that contains predicting utterances. From the data collected in the novel Deception Point, there were 87 data. Type of predictive modality with acceptable were 97.06% (81 data) and less acceptable translation were 2.94% (6 data). Then, on the type of hypothetical prediction were 66.67% with acceptable translation and 33.33% with less acceptable, then 100% were type of habitual prediction with an acceptable translation.
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Gottlieb, R., A. M. Litwin, T. L. Mashtare, G. Wilding, C. L. Raczyk, J. Taylor, M. G. Fakih, and B. Gupta. "Does qualitative radiologic assessment of tumor response measure up to traditional quantitative scoring methods?" Journal of Clinical Oncology 25, no. 18_suppl (June 20, 2007): 17034. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.17034.
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17034 Background: The Response Evaluation Criteria in Solid Tumors (RECIST) and World Health Organization (WHO) radiologic metrics are the standards for tumor response to therapy. However these methods are difficult to use and are limited in their prediction of clinical outcome. We hypothesize a simpler qualitative assessment of tumor response by CT is as reproducible and predictive of clinical outcome as the RECIST and WHO methods. Methods: This was a retrospective evaluation of 23 patients (11 males, 12 females, mean age 56.1 years, range 40–81 years) with biopsy proven metastatic colo-rectal carcinoma treated at our institution between 2002 and 2006 who did not have their primary tumor resected. Only patients with two consecutive CT examinations separated by at least three weeks were included. Two board certified radiologists, blinded to the other's reads, independently interpreted all CT examinations measuring up to five hepatic lesions on both CT examinations using RECIST and WHO criteria and qualitatively assessing all hepatic metastases, categorizing them as increased, decreased, or unchanged between scans. Clinical outcome, using time to progression of disease (TTP), was measured, utilizing a Cox proportional hazards model, to compare the predictive value of all three scoring systems for those patients starting first line chemotherapy (11 patients) at the time of our analysis. Results: Qualitative assessment resulted in agreement in 21/23 patients (91.3%, kappa = 0.78) classifying hepatic metastases as any increase (2 patients), any decrease (17 patients), or no change (2 patients) between scans compared with agreement in 20/23 patients (87.0%) for RECIST (kappa = 0.62) and WHO ( kappa = 0.67) methods placing patients into partial response (2 patients by RECIST and WHO), stable disease (17 patients by RECIST, 16 patients by WHO), and disease progression (1 patient by RECIST, 2 patients by WHO)categories by accepted criteria. No significant difference in prediction of TTP between methods was found. Conclusions: Our pilot data suggests our qualitative scoring system may be at least as reproducible and predictive of patient clinical outcome as the RECIST and WHO methods. No significant financial relationships to disclose.
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Maheshwari, Somesh. "A study of quantitative and qualitative analysis of standardized speech samples in persons suffering from dysarthria due to various neurological disorder." International Journal of Advances in Medicine 7, no. 10 (September 22, 2020): 1527. http://dx.doi.org/10.18203/2349-3933.ijam20204065.
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Background: Dysarthria is manifested as a disorder of movement, it is important to recognize that sensori-motor integration (with tactile, proprioceptive, and auditory feed-back representing the crucial sensory components) is essential to speech motor control, from this standpoint, most or all dysarthria localized to the central nervous system should be thought of as sensori-motor rather than simply motor disturbances.Methods: This non-interventional, cross-sectional comparative, observational study, conducted in 100 study subjects (50 cases and 50 controls) from March 2016 to February 2017 at MGM medical college and MY hospital Indore, MP, India.Results: The mean age of normal population was 53 years and that of dysarthric population was 55 years. Among the dysarthric group, there were 10 cases of ataxic dysarthria, 23 cases of spastic dysarthria, and 9 cases of hypo kinetic dysarthria. There were 20 cases of mild dysarthria 19 cases of moderate dysarthria and 10 cases of severe dysarthria. In ataxic dysarthria, pitch break was found in 6 out of 10 subjects. It was found that there is negative predictive value 93.33%, and positive predictive value, 77.14% in spastic dysarthria and negative predictive value, 83.33% and positive predictive value, 90.90% in ataxic, whereas negative predictive value, 85.71% and positive predictive value, 95.34% in hypo kinetic dysarthria. Conclusions: Different types of dysarthria when analyzed with software tool after extracting pitch and formants showed specific patterns. These patterns correlated with the clinical diagnosis. And Pattern recognition of different dysarthria will help to identify the types of dysarthria in scientific way and prevent inter-subject variability.
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Choi, Jinwook, Yongmoo Suh, and Namchul Jung. "Predicting corporate credit rating based on qualitative information of MD&A transformed using document vectorization techniques." Data Technologies and Applications 54, no. 2 (March 13, 2020): 151–68. http://dx.doi.org/10.1108/dta-08-2019-0127.
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PurposeThe purpose of this study is to investigate the effectiveness of qualitative information extracted from firm’s annual report in predicting corporate credit rating. Qualitative information represented by published reports or management interview has been known as an important source in addition to quantitative information represented by financial values in assigning corporate credit rating in practice. Nevertheless, prior studies have room for further research in that they rarely employed qualitative information in developing prediction model of corporate credit rating.Design/methodology/approachThis study adopted three document vectorization methods, Bag-Of-Words (BOW), Word to Vector (Word2Vec) and Document to Vector (Doc2Vec), to transform an unstructured textual data into a numeric vector, so that Machine Learning (ML) algorithms accept it as an input. For the experiments, we used the corpus of Management’s Discussion and Analysis (MD&A) section in 10-K financial reports as well as financial variables and corporate credit rating data.FindingsExperimental results from a series of multi-class classification experiments show the predictive models trained by both financial variables and vectors extracted from MD&A data outperform the benchmark models trained only by traditional financial variables.Originality/valueThis study proposed a new approach for corporate credit rating prediction by using qualitative information extracted from MD&A documents as an input to ML-based prediction models. Also, this research adopted and compared three textual vectorization methods in the domain of corporate credit rating prediction and showed that BOW mostly outperformed Word2Vec and Doc2Vec.
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Mao, Jiaji, Dabiao Deng, Zehong Yang, Wensheng Wang, Minghui Cao, Yun Huang, and Jun Shen. "Pretreatment structural and arterial spin labeling MRI is predictive for p53 mutation in high-grade gliomas." British Journal of Radiology 93, no. 1115 (November 1, 2020): 20200661. http://dx.doi.org/10.1259/bjr.20200661.
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Objectives: To determine the performance of pretreatment structural and arterial spin labelling (ASL) MRI in predicting p53 mutation in patients with high-grade gliomas (HGGs). Methods: Pre-treatment structural and ASL MRI were performed in 57 patients with histologically confirmed HGGs and information of p53 status. Whole-lesion histogram analysis of cerebral blood flow (CBF) images of the enhancing tumour and the peritumoral oedema in the HGGs were performed. Visually AcceSAble Rembrandt Images features were used as qualitative analysis. The differences of ASL histogram parameters and Visually AcceSAble Rembrandt Images features between HGGs with or without p53 mutation were analyzed with post hoc correction for multiple comparisons. LASSO regression was performed to select the optimal features that could predict p53 mutation, followed by receiver operating characteristic analysis to determine the predictive efficacy. Results: A total of 33 HGGs with p53 mutation and 24 without p53 mutation were included. HGGs with mutant p53 showed lower CBFpercentile5 and CBFuniformity of the enhancing tumour (p < 0.05) and higher prevalence of the qualitative MRI feature of enhancing tumour crossing midline (ETCM) (p < 0.05) as compared with HGGs with wild-type p53. LASSO regression showed that the CBFuniformity of the enhancing tumour and ETCM were predictive features for p53 mutation. CBFuniformity showed an acceptable performance in predicting p53 mutation (area under the curve = 0.721), when combined with the feature of ETCM, its predictive efficacy was significantly improved (area under the curve = 0.814, p = 0.012). Conclusion: An integrated pre-treatment structural and ASL MRI can help to predict p53 mutation in HGGs.
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Papatheodorou, Andreas, and Nikolaos Pappas. "Economic Recession, Job Vulnerability, and Tourism Decision Making: A Qualitative Comparative Analysis." Journal of Travel Research 56, no. 5 (June 9, 2016): 663–77. http://dx.doi.org/10.1177/0047287516651334.
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Occupational uncertainty has a considerable effect upon consumer decisions during a recession, especially with respect to discretionary products and services such as tourism. Using Qualitative Comparative Analysis (QCA), the study examines the complex relations among job vulnerability, disposable income for tourism, marketing activities, and price and quality issues for Greek holiday makers returning from their vacations. The article also compares QCA with the two dominant linear methods of analysis (i.e. correlation and regression) and highlights the suitability of QCA when dealing with complexity in tourism. The results reveal four configurations explaining the attributes of Greek residents’ tourism decisions, characterized by value-for-money orientation, achievement of best available purchase, psychological strengthening, and price sensitivity. The study also employs predictive validity for the presented models. The findings are valid from both a methodological and managerial perspective suggesting new research insights.
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Hasan, Tamanna Binta, Shoibul Karim, Mohammad Ismail Hossain, Md Quamrul Hassan, Sayeda Nasreen, Pradip Bhattacharjee, and Md Zillur Rahman. "Qualitative and Quantitative Analysis of AgNORs in FNAC Smears of Palpable Breast Lumps." Chattagram Maa-O-Shishu Hospital Medical College Journal 15, no. 2 (March 6, 2017): 14–20. http://dx.doi.org/10.3329/cmoshmcj.v15i2.31799.
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Background: The study aimed to evaluate the efficacy of AgNORs on FNAC smears of breast lesion.Methods: FNAC were done in 200 female patients of breast lump consecutively, Papanicolau and AgNOR staining was done in all 200 cases of FNA smears.Among them biopsy and histopathology were done in 99 cases. The findings of FNAC and AgNOR analysis were compared to histopathological findings.Results: By FNAC, 53 (26.5%) cases were non-neoplastic benign, 89(44.5%) cases were benign neoplasm, 07 (3.5%) cases were atypical ductal hyperplasia and 51(25.5%) cases were malignant (Duct cell carcinoma).Among them histopathology was done in 99 cases.Out of 40 FNAC malignant cases, histopathologically all were proved malignant. Out of 42 benign cases, 1 was found malignant. 4 atypical ductal hyperplasia were also diagnosed as malignant histopathologically. True positive cases are 40, True negative cases are 36, false positive are nil (0), and false negative is 01. The sensitivity is 97.56%, specificity is 100%, positive predictive value is 100%, Negative predictive value is 97.3%, and accuracy is 98.7%. AgNOR impression were analysed in 99 histopathologically confirmed cases. The results showed benign impression in 50 cases and malignant impression in 48 cases. 01 histopathologically malignant case was impressed as benign by AgNOR and 01 as suspicious which may be included as malignant by AgNOR impression. 2 histopathologically benign cases showed higher proliferative activities and counted as malignant. True positive cases are 46, True negative 49, false positive is 02 and false negative is 01. The sensitivity is 97.87%, Specificity 96.07%, PPV 95.83%, NPV 98%, Overall accuracy is 96.93%.Conclusion: The efficacy of FNAC and AgNOR analysis in the diagnosis of breast lesion was found more or less similar and mild over lapping.Chatt Maa Shi Hosp Med Coll J; Vol.15 (2); Jul 2016; Page 14-20
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XIA, Qi, Lei-ming YUAN, Xiaojing CHEN, Liuwei MENG, and Guangzao HUANG. "Analysis of Methanol Gasoline by ATR-FT-IR Spectroscopy." Applied Sciences 9, no. 24 (December 6, 2019): 5336. http://dx.doi.org/10.3390/app9245336.
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Methanol gasoline blends are a more economical, and environmentally friendly fuels than gasoline alone, and are widely used in the transportation industry. The content of methanol in methanol gasoline plays an important role in ensuring the quality of gasoline. In some solutions, due to the shortage of energy and illegal profits, the problem of gasoline adulteration and its fineness, has received more and more attention, which would seriously affect the operating condition and service life of internal combustion engines. Therefore, it is very important to identify the correct level of gasoline. However, the traditional detection method is complex and time-consuming. To this end, the feasibility of using attenuated total reflectance Fourier transform infrared (ATR-FTIR) methods coupled with chemometrics methods were investigated to quantitatively and qualitatively analyze methanol gasoline. The qualitative analysis result of partial least squares discriminant analysis (PLS-DA) obtained 100% and 98.66% accuracy in the calibration set and the prediction set, respectively. As for quantitative analysis; two regression algorithms of partial least squares regression (PLSR) and the least square support vector machine (LS-SVM), as well as two variables selection methods of the successive projections algorithm (UVE) competitive adaptive reweighted sampling (CARS) and uninformative variable elimination (UVE) were combined to establish the quantitative model. By comparing the performance of the optimal models; the UVE-PLSR model performed best with a residual predictive deviation (RPD) value of 6.420. The qualitative and quantitative analysis results demonstrate the feasibility of using ATR-FTIR spectra to detect the methanol in methanol gasoline. It is believed that the promising IR spectra will be widely used in gasoline energy quality control in the further.
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García-Pascual, Fernando, Carlos Pérez-Campos, Joaquín García Sánchez, Ana Soto-Rubio, and Sergio Aguado Berenguer. "Models of Sports Management in Fitness Centres. Influence of Sex, Age and Sport Frequency. Linear Models vs. Qualitative Comparative Analysis." Sustainability 13, no. 16 (August 11, 2021): 8995. http://dx.doi.org/10.3390/su13168995.
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Knowing the perceptions of users of sports services has always been an object of analysis within sports management. This paper attempts to analyse what influences the satisfaction and future intentions of fitness centre customers, beyond management variables, by using two different methodologies. A sample of 389 users of a private sports centre was used. Both linear relationships between variables and the combination of sets were analysed using fuzzy set Qualitative Comparative Analysis fsQCA. It is concluded that management variables (service quality, satisfaction and perceived value) are very important for the prediction of management models, but that, according to the interaction methodology between variables, both frequency and sociodemographic variables play an important role in achieving satisfied and loyal users of the sports service. For the prediction of customers’ future intentions, within the analysed sets, it is observed that satisfaction and perceived value are the most predictive variables (raw coverage 0.66). Therefore, and as a consequence, a high number of satisfied and loyal users of the service will allow the economic viability of this service to be achieved.
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Simón-Moya, Virginia, and Lorenzo Revuelto-Taboada. "Revising the predictive capability of business plan quality for new firm survival using qualitative comparative analysis." Journal of Business Research 69, no. 4 (April 2016): 1351–56. http://dx.doi.org/10.1016/j.jbusres.2015.10.106.
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Benda, Natalie C., Lala Tanmoy Das, Erika L. Abramson, Katherine Blackburn, Amy Thoman, Rainu Kaushal, Yongkang Zhang, and Jessica S. Ancker. "“How did you get to this number?” Stakeholder needs for implementing predictive analytics: a pre-implementation qualitative study." Journal of the American Medical Informatics Association 27, no. 5 (March 6, 2020): 709–16. http://dx.doi.org/10.1093/jamia/ocaa021.
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Abstract Objective Predictive analytics are potentially powerful tools, but to improve healthcare delivery, they must be carefully integrated into healthcare organizations. Our objective was to identify facilitators, challenges, and recommendations for implementing a novel predictive algorithm which aims to prospectively identify patients with high preventable utilization to proactively involve them in preventative interventions. Materials and Methods In preparation for implementing the predictive algorithm in 3 organizations, we interviewed 3 stakeholder groups: health systems operations (eg, chief medical officers, department chairs), informatics personnel, and potential end users (eg, physicians, nurses, social workers). We applied thematic analysis to derive key themes and categorize them into the dimensions of Sittig and Singh’s original sociotechnical model for studying health information technology in complex adaptive healthcare systems. Recruiting and analysis were conducted iteratively until thematic saturation was achieved. Results Forty-nine interviews were conducted in 3 healthcare organizations. Technical components of the implementation (hardware and software) raised fewer concerns than alignment with sociotechnical factors. Stakeholders wanted decision support based on the algorithm to be clear and actionable and incorporated into current workflows. However, how to make this disease-independent classification tool actionable was perceived as a challenge, and appropriate patient interventions informed by the algorithm appeared likely to require substantial external and institutional resources. Stakeholders also described the criticality of trust, credibility, and interpretability of the predictive algorithm. Conclusions Although predictive analytics can classify patients with high accuracy, they cannot advance healthcare processes and outcomes without careful implementation that takes into account the sociotechnical system. Key stakeholders have strong perceptions about facilitators and challenges to shape successful implementation.
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Li, Xin, Feng Gao, Fan Li, Xiao-xia Han, Si-hui Shao, Ming-hua Yao, Chun-xiao Li, Jun Zheng, Rong Wu, and Lian-fang Du. "Qualitative analysis of contrast-enhanced ultrasound in the diagnosis of small, TR3–5 benign and malignant thyroid nodules measuring ≤1 cm." British Journal of Radiology 93, no. 1111 (July 2020): 20190923. http://dx.doi.org/10.1259/bjr.20190923.
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Objective: To evaluate the performance of contrast-enhanced ultrasound in the diagnosis of small, solid, TR3–5 benign and malignant thyroid nodules (≤1 cm). Methods: From January 2016 to March 2018, 185 thyroid nodules from 154 patients who underwent contrast enhanced ultrasound (CEUS) and fine-needle aspiration or thyroidectomy in Shanghai General Hospital were included. The χ2 test was used to compare the CEUS characteristics of benign and malignant thyroid nodules, and the CEUS features of malignant nodules assigned scores. The total score of the CEUS features and the scores of the above nodules were evaluated according to the latest 2017 version of the Thyroid Imaging Reporting and Data System (TI-RADS). The diagnostic performance of the two were compared based on the receiver operating characteristic curves generated for benign and malignant thyroid nodules. Results: The degree, enhancement patterns, boundary, shape, and homogeneity of enhancement in thyroid small solid nodules were significantly different (p＜0.05). No significant differences were seen between benign and malignant thyroid nodules regarding completeness of enhancement and size of enhanced lesions (p＞0.05). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the TI-RADS classification TR5 in diagnosis of malignant nodules were 90.10%, 55.95%, 74.59%, 72.22%, and 82.46%, respectively (area under the curve [AUC]=0.738; 95% confidence interval[CI], 0.663–0.813). The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of the total score of CEUS qualitative analysis indicators were 86.13%, 89.29%, 87.57%, 90.63%, and 84.27% respectively (AUC = 0.916; 95% CI, 0.871–0.961). Conclusion: CEUS qualitative analysis is superior to TI-RADS in evaluating the diagnostic performance of small, solid thyroid nodules. Qualitative analysis of CEUS has a significantly higher specificity for diagnosis of malignant thyroid nodules than TI-RADS. Advances in knowledge: The 2017 version of TI-RADS has recently suggested the malignant stratification of thyroid nodules by ultrasound. In this paper we applied this system and CEUS to evaluate 185 nodules and compare the results with pathological findings to access the diagnostic performance.
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Garvey, Aisling A., Andreea M. Pavel, John M. O’Toole, Brian H. Walsh, Irina Korotchikova, Vicki Livingstone, Eugene M. Dempsey, Deirdre M. Murray, and Geraldine B. Boylan. "Multichannel EEG abnormalities during the first 6 hours in infants with mild hypoxic–ischaemic encephalopathy." Pediatric Research 90, no. 1 (April 20, 2021): 117–24. http://dx.doi.org/10.1038/s41390-021-01412-x.
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Abstract Background Infants with mild HIE are at risk of significant disability at follow-up. In the pre-therapeutic hypothermia (TH) era, electroencephalography (EEG) within 6 hours of birth was most predictive of outcome. This study aims to identify and describe features of early EEG and heart rate variability (HRV) (<6 hours of age) in infants with mild HIE compared to healthy term infants. Methods Infants >36 weeks with mild HIE, not undergoing TH, with EEG before 6 hours of age were identified from 4 prospective cohort studies conducted in the Cork University Maternity Services, Ireland (2003–2019). Control infants were taken from a contemporaneous study examining brain activity in healthy term infants. EEGs were qualitatively analysed by two neonatal neurophysiologists and quantitatively assessed using multiple features of amplitude, spectral shape and inter-hemispheric connectivity. Quantitative features of HRV were assessed in both the groups. Results Fifty-eight infants with mild HIE and sixteen healthy term infants were included. Seventy-two percent of infants with mild HIE had at least one abnormal EEG feature on qualitative analysis and quantitative EEG analysis revealed significant differences in spectral features between the two groups. HRV analysis did not differentiate between the groups. Conclusions Qualitative and quantitative analysis of the EEG before 6 hours of age identified abnormal EEG features in mild HIE, which could aid in the objective identification of cases for future TH trials in mild HIE. Impact Infants with mild HIE currently do not meet selection criteria for TH yet may be at risk of significant disability at follow-up. In the pre-TH era, EEG within 6 hours of birth was most predictive of outcome; however, TH has delayed this predictive value. 72% of infants with mild HIE had at least one abnormal EEG feature in the first 6 hours on qualitative assessment. Quantitative EEG analysis revealed significant differences in spectral features between infants with mild HIE and healthy term infants. Quantitative EEG features may aid in the objective identification of cases for future TH trials in mild HIE.
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Ben Bouallègue, Zied. "Calibrated Short-Range Ensemble Precipitation Forecasts Using Extended Logistic Regression with Interaction Terms." Weather and Forecasting 28, no. 2 (April 1, 2013): 515–24. http://dx.doi.org/10.1175/waf-d-12-00062.1.
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Abstract Extended logistic regression has been shown to be a method well suited to calibrating precipitation forecasts from medium-range ensemble prediction systems. The extension of the logistic regression unifies the separate predictive equations for each threshold, introducing the predictive threshold as part of the predictors. Mutually consistent probabilities and a reduction in the total number of regression parameters to be evaluated are part of the benefits of the extended approach. However, considering the predictive threshold as the only “unification” predictor constrains the regression parameters associated with the primary predictors to be constant with the threshold. To alleviate the rigidity of the extended scheme, interaction terms are introduced in the unified predictive equation. Within the framework of the convection-permitting German-focused Consortium for Small-Scale Modeling ensemble prediction system (COSMO-DE-EPS), it is shown that extended logistic regression, applied to short-range precipitation forecasts with the ensemble mean as the primary predictor, improves the performance of the system. Interaction effects are first illustrated through the analysis of regression parameters and then the positive impact on the calibrated forecasts of the new extended logistic regression scheme, including interaction terms, is shown using quantitative and qualitative measures of reliability and sharpness.
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Fordyce, Erin, Michael J. Stern, Sabrina Avripas Bauroth, and Catherine Vladutiu. "Validation of Metrics – A Comparative Analysis of Predictive- and Criterion-Based Validation Tests in a Qualitative Study." Survey Practice 10, no. 1 (January 1, 2017): 1–8. http://dx.doi.org/10.29115/sp-2017-0006.
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Ding, Hua, Liangliang Yang, and Zhaojian Yang. "A Predictive Maintenance Method for Shearer Key Parts Based on Qualitative and Quantitative Analysis of Monitoring Data." IEEE Access 7 (2019): 108684–702. http://dx.doi.org/10.1109/access.2019.2933676.
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Verduin, Maikel, Inge Compter, Sergey Primakov, Sander van Kuijk, Maarten te Dorsthorst, Elles Revenich, Mark ter Laan, et al. "NIMG-65. PREDICTING PROGNOSIS AND CANCER HOTSPOT MUTATIONS USING QUALITATIVE MR IMAGING ANALYSIS IN GLIOBLASTOMA." Neuro-Oncology 21, Supplement_6 (November 2019): vi176. http://dx.doi.org/10.1093/neuonc/noz175.734.
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Abstract INTRODUCTION Tumor heterogeneity poses one of the major limitations in improving the treatment for glioblastoma (GBM), which calls for new clinically relevant predictive models. This study aims to investigate non-invasive diagnostic methods, including patient characteristics and qualitative imaging analysis as a prognostic classifier and predictor for druggable oncogenes. METHODS We performed a retrospective analysis on 143 GBM patients (discovery cohort). Diagnostic MRIs were re-analyzed for qualitative imaging features (VASARI features). DNA was extracted from formalin-fixed, paraffin-embedded GBM tissue of the discovery cohort for next-generation sequencing (Ion Torrent Cancer Hotspot panel v2Plus), TERT-promoter mutation and MGMT-methylation analysis. Multivariable regression analysis was used to determine the prognostic and predictive value of VASARI features. RESULTS Of the 143 patients, median age was 61.4 years (range 15.5–84.6) with a median overall survival of 12 months (range 0–142). We observed IDH1 R132H mutation in 8.5%, MGMT-promotor methylation in 26.1%, TERT-promotor mutation (C250T;C228T) in 69.5%, EGFR mutation in 20.3% and EGFR amplification in 37.5% of all patients. A set of eight VASARI features was identified to be associated with overall survival (p< 0.001), which is currently being validated in an external dataset (n= 184). Interestingly, VASARI features appeared to be associated with IDH1-mutation (four features, p=0.004), TERT-promotor mutation (five features, p-value < 0.001), EGFR mutation (five features, p-value < 0.001) and EGFR amplification (seven features, p-value < 0.001) but not with MGMT-methylation (two features, p-value=0.054). Additional cancer hotspots are currently being analyzed and internal validation is ongoing. CONCLUSION AND FUTURE PERSPECTIVES We propose an integrated prognostic classifier comprising MRI features, also associated with GBM-specific molecular alterations. Additionally, quantitative MRI radiomics features are being extracted from the discovery and validation set and incorporated in the prognostic classifier. Subsequently, radiomics and VASARI features will be correlated to intratumoral heterogeneity, assessed by tissue micro-array analysis of the discovery cohort.
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Wang, Fuyun, Hao Lin, Peiting Xu, Xiakun Bi, and Li Sun. "Egg Freshness Evaluation Using Transmission and Reflection of NIR Spectroscopy Coupled Multivariate Analysis." Foods 10, no. 9 (September 14, 2021): 2176. http://dx.doi.org/10.3390/foods10092176.
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This work presents a novel work for the detection of the freshness of eggs stored at room temperature and refrigerated conditions by the near-infrared (NIR) spectroscopy and multivariate models. The NIR spectroscopy of diffuse transmission and reflection modes was used to compare the quantitative and qualitative investigation of egg freshness. It was found that diffuse transmission is more conducive to the judgment of egg freshness. The linear discriminant analysis model (LDA) for pattern recognition based on the diffuse transmission measurement was employed to analyze egg freshness during storage. NIR diffuse transmission spectroscopy showed great potential for egg storage time discrimination in normal atmospheric conditions. The LDA model discrimination rated up to 91.4% in the prediction set, while only 25.6% of samples were correctly discriminated among eggs in refrigerated storage conditions. Furthermore, NIR spectra, combined with the synergy interval partial least squares (Si-PLS) model, showed excellent ability in egg physical index prediction under normal atmospheric conditions. The root means square error of prediction (RMSEP) values of Haugh unit, yolk index, and weight loss from predictive Si-PLS models were 4.25, 0.031, and 0.005432, respectively.
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Bedford, Michael, Paul Stevens, Simon Coulton, Jenny Billings, Marc Farr, Toby Wheeler, Maria Kalli, Tim Mottishaw, and Chris Farmer. "Development of risk models for the prediction of new or worsening acute kidney injury on or during hospital admission: a cohort and nested study." Health Services and Delivery Research 4, no. 6 (February 2016): 1–160. http://dx.doi.org/10.3310/hsdr04060.
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BackgroundAcute kidney injury (AKI) is a common clinical problem with significant morbidity and mortality. All hospitalised patients are at risk. AKI is often preventable and reversible; however, the 2009 National Confidential Enquiry into Patient Outcome and Death highlighted systematic failings of identification and management, and recommended risk assessment of all emergency admissions.ObjectivesTo develop three predictive models to stratify the risk of (1) AKI on arrival in hospital; (2) developing AKI during admission; and (3) worsening AKI if already present; and also to (4) develop a clinical algorithm for patients admitted to hospital and explore effective methods of delivery of this information at the point of care.Study designQuantitative methodology (1) to formulate predictive risk models and (2) to validate the models in both our population and a second population. Qualitative methodology to plan clinical decision support system (CDSS) development and effective integration into clinical care.Settings and participantsQuantitative analysis – the study population comprised hospital admissions to three acute hospitals of East Kent Hospitals University NHS Foundation Trust in 2011, excluding maternity and elective admissions. For validation in a second population the study included hospital admissions to Medway NHS Foundation Trust. Qualitative analysis – the sample consisted of six renal consultants (interviews) and six outreach nurses (focus group), with representation from all sites.Data collectionData (comprising age, sex, comorbidities, hospital admission and outpatient history, relevant pathology tests, drug history, baseline creatinine and chronic kidney disease stage, proteinuria, operative procedures and microbiology) were collected from the hospital data warehouse and the pathology and surgical procedure databases.Data analysisQuantitative – both traditional and Bayesian regression methods were used. Traditional methods were performed using ordinal logistic regression with univariable analyses to inform the development of multivariable analyses. Backwards selection was used to retain only statistically significant variables in the final models. The models were validated using actual and predicted probabilities, an area under the receiver operating characteristic (AUROC) curve analysis and the Hosmer–Lemeshow test. Qualitative – content analysis was employed.Main outcome measures(1) A clinical pratice algorithm to guide clinical alerting and risk modeling for AKI in emergency hospital admissions; (2) identification of the key variables that are associated with the risk of AKI; (3) validated risk models for AKI in acute hospital admissions; and (4) a qualitative analysis providing guidance as to the best approach to the implementation of clinical alerting to highlight patients at risk of AKI in hospitals.FindingsQuantitative – we have defined a clinical practice algorithm for risk assessment within the first 24 hours of hospital admission. Bayesian methodology enabled prediction of low risk but could not reliably identify high-risk patients. Traditional methods identified key variables, which predict AKI both on admission and at 72 hours post admission. Validation demonstrated an AUROC curve of 0.75 and 0.68, respectively. Predicting worsening AKI during admission was unsuccessful. Qualitative – analysis of AKI alerting gave valuable insights in terms of user friendliness, information availability, clinical communication and clinical responsibility, and has informed CDSS development.ConclusionsThis study provides valuable evidence of relationships between key variables and AKI. We have developed a clinical algorithm and risk models for risk assessment within the first 24 hours of hospital admission. However, the study has its limitations, and further analysis and testing, including continuous modelling, non-linear modelling and interaction exploration, may further refine the models. The qualitative study has highlighted the complexity regarding the implementation and delivery of alerting systems in clinical practice.FundingThe National Institute for Health Research Health Services and Delivery Research programme.
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Platão Seabra Paiva, Luã, Livia Da Silva Oliveira, David Barbosa de Alencar, and Paulo Oliveira Siqueira Júnior. "Predictive Maintenance Through Thermographic Analysis: Case Study in a Manaus Industrial Pole Company." International Journal for Innovation Education and Research 7, no. 11 (November 30, 2019): 898–909. http://dx.doi.org/10.31686/ijier.vol7.iss11.1945.
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Due to the high demand for electricity in the manufacturing industry, companies to obtain greater profitability on their produced goods, seek and adopt ways to reduce energy consumption, and use predictive maintenance as a tool by applying thermography. Thus, the purpose of the research is to show the importance of thermographic analysis for assessing losses and preserving the safety of the company's physical facilities. The research is descriptive, qualitative and case study. The instrument used for data collection were direct observation and document analysis. In this context, the results obtained were the mapping in the manufacturing facilities and the identification of some failures in the company's electrical system. After this data collection process, it was possible to analyze and plan the corrective actions. In conclusion, it is possible to reduce manufacturing costs through predictive maintenance through the thermographic analysis tool, positively impacting the company's financial results.
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La Forgia, Daniele, Angela Vestito, Maurilia Lasciarrea, Maria Colomba Comes, Sergio Diotaiuti, Francesco Giotta, Agnese Latorre, et al. "Response Predictivity to Neoadjuvant Therapies in Breast Cancer: A Qualitative Analysis of Background Parenchymal Enhancement in DCE-MRI." Journal of Personalized Medicine 11, no. 4 (April 1, 2021): 256. http://dx.doi.org/10.3390/jpm11040256.
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Background: For assessing the predictability of oncology neoadjuvant therapy results, the background parenchymal enhancement (BPE) parameter in breast magnetic resonance imaging (MRI) has acquired increased interest. This work aims to qualitatively evaluate the BPE parameter as a potential predictive marker for neoadjuvant therapy. Method: Three radiologists examined, in triple-blind modality, the MRIs of 80 patients performed before the start of chemotherapy, after three months from the start of treatment, and after surgery. They identified the portion of fibroglandular tissue (FGT) and BPE of the contralateral breast to the tumor in the basal control pre-treatment (baseline). Results: We observed a reduction of BPE classes in serial MRI checks performed during neoadjuvant therapy, as compared to baseline pre-treatment conditions, in 61.3% of patients in the intermediate step, and in 86.7% of patients in the final step. BPE reduction was significantly associated with sequential anthracyclines/taxane administration in the first cycle of neoadjuvant therapy compared to anti-HER2 containing therapies. The therapy response was also significantly related to tumor size. There were no associations with menopausal status, fibroglandular tissue (FGT) amount, age, BPE baseline, BPE in intermediate, and in the final MRI step. Conclusions: The measured variability of this parameter during therapy could predict therapy effectiveness in early stages, improving decision-making in the perspective of personalized medicine. Our preliminary results suggest that BPE may represent a predictive factor in response to neoadjuvant therapy in breast cancer, warranting future investigations in conjunction with radiomics.
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Rivera-Navarro, Jesús, Esther Cubo, and Natividad Mariscal. "Analysis of the Reasons for Non-Uptake of Predictive Testing for Huntington’s Disease in Spain: A Qualitative Study." Journal of Genetic Counseling 24, no. 6 (April 30, 2015): 1011–21. http://dx.doi.org/10.1007/s10897-015-9840-x.
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Zaid, Mohamed, Lauren Widmann, Annie Dai, Kevin Sun, Jie Zhang, Jun Zhao, Mark W. Hurd, et al. "Predictive Modeling for Voxel-Based Quantification of Imaging-Based Subtypes of Pancreatic Ductal Adenocarcinoma (PDAC): A Multi-Institutional Study." Cancers 12, no. 12 (December 5, 2020): 3656. http://dx.doi.org/10.3390/cancers12123656.
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Previously, we characterized qualitative imaging-based subtypes of pancreatic ductal adenocarcinoma (PDAC) on computed tomography (CT) scans. Conspicuous (high delta) PDAC tumors are more likely to have aggressive biology and poorer clinical outcomes compared to inconspicuous (low delta) tumors. Here, we developed a quantitative classification of this imaging-based subtype (quantitative delta; q-delta). Retrospectively, baseline pancreatic protocol CT scans of three cohorts (cohort#1 = 101, cohort#2 = 90 and cohort#3 = 16 [external validation]) of patients with PDAC were qualitatively classified into high and low delta. We used a voxel-based method to volumetrically quantify tumor enhancement while referencing normal-pancreatic-parenchyma and used machine learning-based analysis to build a predictive model. In addition, we quantified the stromal content using hematoxylin- and eosin-stained treatment-naïve PDAC sections. Analyses revealed that PDAC quantitative enhancement values are predictive of the qualitative delta scoring and were used to build a classification model (q-delta). Compared to high q-delta, low q-delta tumors were associated with improved outcomes, and the q-delta class was an independent prognostic factor for survival. In addition, low q-delta tumors had higher stromal content and lower cellularity compared to high q-delta tumors. Our results suggest that q-delta classification provides a clinically and biologically relevant tool that may be integrated into ongoing and future clinical trials.
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Kennedy, Betsy, Mary Dietrich, Lorraine Mion, Laurie Novak, and Alvin Jeffery. "A Qualitative Exploration of Nurses’ Information-Gathering Behaviors Prior to Decision Support Tool Design." Applied Clinical Informatics 08, no. 03 (2017): 763–78. http://dx.doi.org/10.4338/aci-2017-02-ra-0033.
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Summary Background: Large and readily-available clinical datasets combined with improved computational resources have permitted the exploration of many new research and clinical questions. Predictive analytics, especially for adverse events, has surfaced as one promising application of big data, and although statistical results can be highly accurate, little is known about how nurses perceive this new information and how they might act upon it. Objectives: Within the context of recognizing patients at risk for cardiopulmonary arrest, this study explored the possibility of incorporating predictive analytics into clinical workflows by identifying nurses’ current information gathering activities and perceptions of probability-related terms. Methods: We used a qualitative description approach for data collection and analysis in order to understand participants’ information gathering behaviors and term perceptions in their own words. We conducted one-on-one interviews and a focus group with a total of 10 direct care bedside nurses and 8 charge nurses. Results: Participants collected information from many sources that we categorized as: Patient, Other People, and Technology. The process by which they gathered information was conducted in an inconsistent order and differed by role. Major themes comprised: (a) attempts to find information from additional sources during uncertainty, (b) always being prepared for the worst-case scenario, and (c) the desire to review more detailed predictions. Use of the words probability, risk, and uncertainty were inconsistent. Conclusions: In an effort to successfully incorporate predictive analytics into clinical workflows, we have described nurses’ perceived work practices for gathering information related to clinical deterioration and nurses’ beliefs related to probability-based information. Findings from our study could guide design and implementation efforts of predictive analytics in the clinical arena.Jeffery AD, Kennedy B, Dietrich MS, Mion LC, Novak LL. A Qualitative Exploration of Nurses’ Information-Gathering Behaviors Prior to Decision Support Tool Design. Appl Clin Inform 2017; 8: 763–778 https://doi.org/10.4338/ACI-2017-02-RA-0033
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Anthony, Jared S., Karen E. Clayton, and Akane Zusho. "An Investigation of Students’ Self-Regulated Learning Strategies: Students’ Qualitative and Quantitative Accounts of Their Learning Strategies." Journal of Cognitive Education and Psychology 12, no. 3 (2013): 359–73. http://dx.doi.org/10.1891/1945-8959.12.3.359.
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The purpose of this study was to examine the relationship between qualitative and quantitative measures of self-regulatory learning strategies to further investigate issues related to the validity of self-report measures. One hundred and sixty high school girls completed both the Motivated Strategies for Learning Questionnaire (MSLQ) and an open-ended questionnaire, both of which were designed to assess students’ use of learning strategies in the domains of English and math. Open-ended responses were coded and analyzed with results indicating that most students use shallow-processing strategies when preparing for final exams. Regression analysis was also used to investigate the predictive ability of the MSLQ and the open-ended questionnaire with findings indicating both to have predictive qualities. Implications for self-regulation and the measurement of learning strategies will be discussed.
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Sahin, Hilal, Janette Smith, Jeries Paolo Zawaideh, Amreen Shakur, Luca Carmisciano, Iztok Caglic, Annemarie Bruining, Mercedes Jimenez-Linan, Sue Freeman, and Helen Addley. "Diagnostic interpretation of non-contrast qualitative MR imaging features for characterisation of uterine leiomyosarcoma." British Journal of Radiology 94, no. 1125 (September 1, 2021): 20210115. http://dx.doi.org/10.1259/bjr.20210115.
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Objective: To assess the value of non-contrast MRI features for characterisation of uterine leiomyosarcoma (LMS) and differentiation from atypical benign leiomyomas Methods: This study included 57 atypical leiomyomas and 16 LMS which were referred pre-operatively for management review to the specialist gynaeoncology multidisciplinary team meeting. Non-contrast MRIs were retrospectively reviewed by five independent readers (three senior, two junior) and a 5-level Likert score (1-low/5-high) was assigned to each mass for likelihood of LMS. Evaluation of qualitative and quantitative MRI features was done using uni- and multivariable regression analysis. Inter-reader reliability for the assessment of MRI features was calculated by using Cohen’s κ values. Results: In the univariate analysis, interruption of the endometrial interface and irregular tumour shape had the highest odds ratios (ORs) (64.00, p < 0.001 and 12.00, p = 0.002, respectively) for prediction of LMS. Likert score of the mass was significant in prediction (OR, 3.14; p < 0.001) with excellent reliability between readers (ICC 0.86; 95% CI, 0.76–0.92). The post-menopausal status, interruption of endometrial interface and thickened endometrial stripe were the most predictive independent variables in multivariable estimation of the risk of leiomyosarcoma with an accuracy of 0.88 (95%CI, 0.78–0.94). Conclusion: At any level of expertise as a radiologist reader, the loss of the normal endometrial stripe (either thickened or not seen) in a post-menopausal patient with a myometrial mass was highly likely to be LMS. Advances in knowledge: This study demonstrates the potential utility of non-contrast MRI features in characterisation of LMS over atypical leiomyomas, and therefore influence on optimal management of these cases.
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Yan, Fei Xue, Jing Xia, Guan Qun Shen, and Xu Sheng Kang. "A Crime Decision-Making Model Based on AHP." Applied Mechanics and Materials 50-51 (February 2011): 885–89. http://dx.doi.org/10.4028/www.scientific.net/amm.50-51.885.
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As time goes by, hazard rate of the society would increase if crime prediction was not implemented. Based on objective factors of offenders and victims characteristics, AHP method can be established to get a quantitative and qualitative analysis on crime prediction. Crime prediction is a strategic and tactical measure for crime prevention. According to AHP analysis, two prediction models of the optimal predictive crime locations are put forward. Standard Deviational Ellipses Model and Key Feature adjusted Spatial Choice Model were formulated to account for the anticipated position with various elements from AHP method. These models could be applied in a computer simulation of situation tests of the series murders. Besides, applying those models in certain real case demonstrates how the models work. Through models comparison, the results are summarized that Key Feature adjusted Spatial Choice Model is more conducive in confirming the guilty place. In conclusion, the suggested models, including detailed criminal map, are easy to implement.
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Below, Catrin von, Cecilia Wassberg, Rafael Grzegorek, Joel Kullberg, Charlotta Gestblom, Jens Sörensen, Mauritz Waldén, and Håkan Ahlström. "MRI and 11C acetate PET/CT for prediction of regional lymph node metastasis in newly diagnosed prostate cancer." Radiology and Oncology 52, no. 1 (January 24, 2018): 90–97. http://dx.doi.org/10.2478/raon-2018-0001.
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Abstract Background The aim of the study was to examine the value of quantitative and qualitative MRI and 11C acetate PET/CT parameters in predicting regional lymph node (LN) metastasis of newly diagnosed prostate cancer (PCa). Patients and methods Patients with intermediate (n = 6) and high risk (n = 47) PCa underwent 3T MRI (40 patients) and 11C acetate PET/CT (53 patients) before extended pelvic LN dissection. For each patient the visually most suspicious LN was assessed for mean apparent diffusion coefficient (ADCmean), maximal standardized uptake value (SUVmax), size and shape and the primary tumour for T stage on MRI and ADCmean and SUVmax in the index lesion. The variables were analysed in simple and multiple logistic regression analysis. Results All variables, except ADCmean and SUVmax of the primary tumor, were independent predictors of LN metastasis. In multiple logistic regression analysis the best model was ADCmean in combintion with MRI T-stage where both were independent predictors of LN metastasis, this combination had an AUC of 0.81 which was higher than the AUC of 0.65 for LN ADCmean alone and the AUC of 0.69 for MRI T-stage alone. Conclusions Several quantitative and qualitative imaging parameters are predictive of regional LN metastasis in PCa. The combination of ADCmean in lymph nodes and T-stage on MRI was the best model in multiple logistic regression with increased predictive value compared to lymph node ADCmean and T-stage on MRI alone.
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Coscas, Florence, Marco Lupidi, Jean François Boulet, Alexandre Sellam, Diogo Cabral, Rita Serra, Catherine Français, Eric H. Souied, and Gabriel Coscas. "Optical coherence tomography angiography in exudative age-related macular degeneration: a predictive model for treatment decisions." British Journal of Ophthalmology 103, no. 9 (November 22, 2018): 1342–46. http://dx.doi.org/10.1136/bjophthalmol-2018-313065.
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AimsTo evaluate on optical coherence tomography angiography (OCT-A), the predictive role of different qualitative findings of choroidal neovascularisations (CNV) in assessing the status of exudative age-related macular degeneration (eAMD) and to develop a potential model to predict the CNV activity.MethodsRetrospective review of the multimodal imaging records of patients with eAMD obtained during treatment for type 1 or type 2 CNV. The qualitative analysis of CNVs on OCT angiograms assessed the presence or absence of tiny branching vessels, loops, peripheral anastomotic arcades and choriocapillaris hypointense halo. These findings were then correlated with those of structural OCT scans. A score forecast was built and validated.ResultsOne hundred and twenty-six eAMD eyes were enrolled in the study. Exudation was observed in 90 eyes (71%) on structural OCT. The qualitative OCT-A analysis revealed: tiny branching vessels in 82.5% of the cases, vascular loops in 81.7%, peripheral anastomotic arcades in 66.7% and choriocapillaris hypointense halo in 54.8%. In the univariate analysis, each OCT-A parameter showed a statistically significant correlation with exudation on structural OCT (p<0.001). The overall analysis demonstrated a sensitivity of 96.7% and a positive predictive value of 87.9%. In the multivariate analysis, a model with four criteria predicted an exudative lesion in 97.6% of cases and one with two criteria (tiny branching vessels and peripheral anastomotic arcades) in 71.2%.ConclusionsThe presence of tiny branching vessels and a peripheral anastomotic arcade appears to predict the lesion activity with a good accuracy and the model based on four criteria enables optimal decisions regarding retreatment in eAMD.
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Solovieva, Yulia, Xaman Rivas, Ignacio Méndez-Balbuena, Regina Machinskaya, and Héctor Juan Pelayo-González. "Neuropsychology and electroencephalography to study attention deficit hyperactivity disorder." Revista de la Facultad de Medicina 64, no. 3 (July 1, 2016): 427. http://dx.doi.org/10.15446/revfacmed.v64n3.54924.
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Introduction: In a previous study carried out with children from first to third grade in an elementary school, the authors of this research evidenced that different profiles of neuropsychological difficulties and functional status of brain structures exist at subcortical and cortical levels. Such results differ from those obtained in preschool children.Objective: To correlate data obtained through neuropsychological assessment and EEG in Mexican children from fourth grade through sixth grade in an elementary school diagnosed with ADHD.Materials and methods: A qualitative syndromic analysis was used to establish predominant neuropsychological mechanisms. A qualitative analysis of EEG was conducted to determine functional and maturational aspects of children’s development.Results: Findings of correlations between neuropsychological and electrophysiological data showed diversity of neuropsychological difficulties and specific EEG patterns. The possibility of high correlation between data of qualitative neuropsychological analysis and functional analysis of electroencephalographic phenomenon is discussed.Conclusions: Final results suggest an important predictive level regarding clinical profiles obtained through the joined work of the clinical qualitative instruments used in this study.
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Spiehler, V., J. Fay, R. Fogerson, D. Schoendorfer, and R. S. Niedbala. "Enzyme immunoassay validation for qualitative detection of cocaine in sweat." Clinical Chemistry 42, no. 1 (January 1, 1996): 34–38. http://dx.doi.org/10.1093/clinchem/42.1.34.
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Abstract A solid-phase enzyme immunoassay (EIA) involving microtiter plates was modified for analysis of cocaine in sweat. Sweat was collected with the PharmChek sweat patch and drugs were eluted from the collection pad of the patch. The sweat contained primarily parent cocaine. The assay was determined to have cross-reactivity for cocaine of 102% relative to 100% for the benzoylecgonine (BE) calibrators and for cocaethylene of 148%. The optimum cutoff concentration for this modified assay, determined by receiver-operating characteristic curve analysis, was 10 micrograms/L cocaine or BE equivalents. At this concentration the assay had 94.5% sensitivity and 99.1% specificity vs gas chromatography-mass spectrometry (GC-MS) as an acceptable indicator of the true clinical state. The positive predictive value at a prevalence of 50% was 99%. Threshold analysis for positives suggested that the 95% confidence interval for a positive result by the EIA was between 12.5 and 15 micrograms/L and that quality-control samples at 5 and 15 micrograms/L could be run with each batch to certify the precision around the cutoff. All positive samples must be confirmed by GC-MS. The sensitivity and specificity of the overall analysis system (immunoassay screen and GC-MS confirmation) was 86% and 97%, with known cocaine dosing of volunteers as the acceptable indicator of the true clinical state.
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Speiser, Phyllis, Reeti Chawla, Ming Chen, Alicia Diaz-Thomas, Courtney Finlayson, Meilan Rutter, David Sandberg, et al. "Newborn Screening Protocols and Positive Predictive Value for Congenital Adrenal Hyperplasia Vary across the United States." International Journal of Neonatal Screening 6, no. 2 (May 8, 2020): 37. http://dx.doi.org/10.3390/ijns6020037.
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Newborn screening for congenital adrenal hyperplasia (CAH) caused by 21-hydroxylase deficiency is mandated throughout the US. Filter paper blood specimens are assayed for 17-hydroxyprogesterone (17OHP). Prematurity, low birth weight, or critical illness cause falsely elevated results. The purpose of this report is to highlight differences in protocols among US state laboratories. We circulated a survey to state laboratory directors requesting qualitative and quantitative information about individual screening programs. Qualitative and quantitative information provided by 17 state programs were available for analysis. Disease prevalence ranged from 1:9941 to 1:28,661 live births. Four state laboratories mandated a second screen regardless of the initial screening results; most others did so for infants in intensive care units. All but one program utilized birthweight cut-points, but cutoffs varied widely: 17OHP values of 25 to 75 ng/mL for birthweights >2250–2500 g. The positive predictive values for normal birthweight infants varied from 0.7% to 50%, with the highest predictive values based in two of the states with a mandatory second screen. Data were unavailable for negative predictive values. These data imply differences in sensitivity and specificity in CAH screening in the US. Standardization of newborn screening protocols could improve the positive predictive value.
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Stauch, C., H. Reiber, M. Rauchenzauner, A. Strasak, D. Pohl, F. Hanefeld, J. Gärtner, and KM Rostásy. "Intrathecal IgM synthesis in pediatric MS is not a negative prognostic marker of disease progression: quantitative versus qualitative IgM analysis." Multiple Sclerosis Journal 17, no. 3 (December 1, 2010): 327–34. http://dx.doi.org/10.1177/1352458510388543.
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Background: Intrathecal IgM synthesis is reported to be associated with a worse prognosis in adults with multiple sclerosis (MS). Objective: To study the predictive value of intrathecal IgM synthesis for the clinical course of pediatric MS. Methods: Seventy children with onset of MS before the age of 16 years and followed for a median period of 10.4 years (range: 0.4–22.8 years) were studied. The two subgroups with ( n = 44) or without ( n = 26) intrathecal IgM synthesis were distinguished by a new, very sensitive, evaluation of quantitative analysis in cerebrospinal fluid (CSF)/serum quotient diagrams (Reibergrams). The clinical course and EDSS (Expanded Disability Status Scale) scores at five and ten years were compared with IgM frequencies between both groups with a new statistics program for CSF data. Results: The cohort of children without intrathecal IgM production had higher numbers of attacks in the first two years and shorter time intervals between first and second attack, although this was not statistically significant ( p = 0.04, p = 0.15 respectively). In addition there was also a trend for girls without intrathecal IgM synthesis to have a higher EDSS score after 10 years compared with the group with IgM synthesis. Conclusion: Intrathecal IgM synthesis is not associated with a more rapid progression of disability in pediatric MS. Reevaluation of data from previous reports about the negative predictive value of intrathecal IgM synthesis in adult MS with a CSF statistics tool show that the apparent contradiction is due to a methodological bias in the qualitative detection of ‘oligoclonal’ IgM or linear IgM index.
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Barton, Henry, Elisabeth Zechendorf, Dirk Ostareck, Antje Ostareck-Lederer, Christian Stoppe, Rashad Zayat, Tim Simon-Philipp, Gernot Marx, and Johannes Bickenbach. "Prognostic Value of GDF-15 in Predicting Prolonged Intensive Care Stay following Cardiac Surgery: A Pilot Study." Disease Markers 2021 (June 15, 2021): 1–10. http://dx.doi.org/10.1155/2021/5564334.
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Introduction. Predicting intensive care unit length of stay and outcome following cardiac surgery is currently based on clinical parameters. Novel biomarkers could be employed to improve the prediction models. Materials and Methods. We performed a qualitative cytokine screening array to identify highly expressed biomarkers in preoperative blood samples of cardiac surgery patients. After identification of one highly expressed biomarker, growth differentiation factor 15 (GDF-15), a quantitative ELISA was undertaken. Preoperative levels of GDF-15 were compared in regard to duration of intensive care stay, cardiopulmonary bypass time, and indicators of organ dysfunction. Results. Preoperatively, GDF-15 was highly expressed in addition to several less highly expressed other biomarkers. After qualitative analysis, we could show that preoperatively raised levels of GDF-15 were positively associated with prolonged ICU stay exceeding 48 h (median 713 versus 1041 pg/ml, p = 0.003 ). It was also associated with prolonged mechanical ventilation and rates of severe sepsis but not with dialysis rates or cardiopulmonary bypass time. In univariate regression, raised GDF-15 levels were predictive of a prolonged ICU stay (OR 1.01, 95% confidence interval 1–1.02, and p = 0.029 ). On ROC curves, GDF-15 was found to predict prolonged ICU stay ( AUC = 0.86 , 95% confidence interval 0.71–0.99, and p = 0.003 ). Conclusion. GDF-15 showed potential as predictor of prolonged intensive care stay following cardiac surgery, which might be valuable for risk stratification models.
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Bekiaris, Georgios, Dimitra Tagkouli, Georgios Koutrotsios, Nick Kalogeropoulos, and Georgios I. Zervakis. "Pleurotus Mushrooms Content in Glucans and Ergosterol Assessed by ATR-FTIR Spectroscopy and Multivariate Analysis." Foods 9, no. 4 (April 24, 2020): 535. http://dx.doi.org/10.3390/foods9040535.
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Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy was used to monitor the infrared absorption spectra of 79 mushroom samples from 29 Pleurotus ostreatus, P. eryngii and P. nebrodensis strains cultivated on wheat straw, grape marc and/or by-products of the olive industry. The spectroscopic analysis provided a chemical insight into the mushrooms examined, while qualitative and quantitative differences in regions related to proteins, phenolic compounds and polysaccharides were revealed among the species and substrates studied. Moreover, by using advanced chemometrics, correlations of the recorded mushrooms’ spectra versus their content in glucans and ergosterol, commonly determined through traditional analytical techniques, allowed the development of models predicting such contents with a good predictive power (R2: 0.80–0.84) and accuracy (low root mean square error, low relative error and representative to the predicted compounds spectral regions used for the calibrations). Findings indicate that FTIR spectroscopy could be exploited as a potential process analytical technology tool in the mushroom industry to characterize mushrooms and to assess their content in bioactive compounds.
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Paukova, Yulia V., and Konstantin V. Popov. "FORECASTING MIGRATION FLOWS USING PREDICTIVE ANALYTICS." SCIENTIFIC REVIEW. SERIES 1. ECONOMICS AND LAW, no. 1-2 (2020): 45–54. http://dx.doi.org/10.26653/2076-4650-2020-1-2-04.
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The present article considers the need to predict migration flows using Predictive Analytics. The Russian Federation is a center of migration activity. The modern world is changing rapidly. An effective migration policy requires effective monitoring of migration flows, assessing the current situation in our and other countries and forecasting migration processes. There are information systems in Russia that contain a wide range of information about foreign citizens and stateless persons that provide the requested information about specific foreign citizens, including grouping it on various grounds. However, it is not possible to analyze and predict it automatically using thousands of parameters. Special attention in Russia is paid to digitalization. Using information technologies (artificial intelligence, machine learning and big data analysis) to forecast migration flows in conditions of variability of future events will allow to take into account a number of events and most accurately predict the quantitative and so-called "qualitative" structure of arrivals. The received information will help to develop state policy and to take appropriate measures in the field of migration regulation. The authors come to the conclusion that it is necessary to amend existing legal acts in order to implement information technologies of Predictive Analytics into the practice of migration authorities.