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Journal articles on the topic 'Medical Text'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 April 2022

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1

Gafurov, Bakhtiyor. "MEDICAL TERMINOLOGY IN ADVERTISING TEXT." Scientific Reports of Bukhara State University 5, no. 3 (June 30, 2021): 30–40. http://dx.doi.org/10.52297/2181-1466/2021/5/3/3.

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Background. The article is dedicated to advertising text, which widely uses medical terminology. Advertising text, in which medical vocabulary is widely used, has its own specific character. The content of pharmaceutical advertising texts is enriched with a wide range of medical terminology and helps the consumer to know what is for what or what is for what. Undoubtedly, the text of advertising for pharmaceutical products can serve as a widespread dissemination of the advertised drug among the population. Taking into account the demand, interests, sometimes the individual desire of consumers, the text of the advertisement is drawn up, referring to the linguistic, linguocultural, linguo-sociological capabilities of each language. The lexicological stock of the language is actively used in the design of the advertising text, which contains medical vocabulary.

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2

Broom, M. A., G. T. Adamson, and L. R. Draper. "Text Messaging in Medical Education." PEDIATRICS 133, no. 3 (February 17, 2014): e491-e493. http://dx.doi.org/10.1542/peds.2013-1529.

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3

De Marco, Barbara. "Philology and the Medical Text." Romance Philology 71, no. 2 (September 2017): iii—iv. http://dx.doi.org/10.1484/j.rph.5.114783.

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4

Smith, A. M. "Medical Law: Text and Materials." Journal of Medical Ethics 16, no. 4 (December 1, 1990): 220. http://dx.doi.org/10.1136/jme.16.4.220.

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5

McHale, J. V. "Medical law: text with materials." Journal of Medical Ethics 21, no. 5 (October 1, 1995): 314–15. http://dx.doi.org/10.1136/jme.21.5.314-a.

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6

Wall, Michael. "Medical terminology: A programmed text." Nurse Education Today 8, no. 4 (August 1988): 244. http://dx.doi.org/10.1016/0260-6917(88)90159-1.

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7

Macgregor, A. J. "Medical law: Text and materials." Journal of Dentistry 19, no. 3 (June 1991): 188. http://dx.doi.org/10.1016/0300-5712(91)90015-q.

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8

Scollon, Ron. "Food and behavior: A Burkean motive analysis of a quasi-medical text." Text & Talk - An Interdisciplinary Journal of Language, Discourse Communication Studies 26, no. 1 (January 26, 2006): 107–25. http://dx.doi.org/10.1515/text.2006.005.

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9

Rossi, Maria Grazia, Fabrizio Macagno, and Sarah Bigi. "Dialogical functions of metaphors in medical interactions." Text & Talk 42, no. 1 (November 11, 2021): 77–103. http://dx.doi.org/10.1515/text-2019-0166.

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Abstract This paper proposes a method for analyzing the dialogical functions of metaphors in communicative interactions, and more specifically in the context of medical interviews. The dialogical goals proposed and pursued by the interlocutors are coded using a coding scheme that captures seven mutually exclusive categories of dialogical moves. The functions of the moves, including metaphors, can be identified and correlated with other variables relevant to the type of communication under analysis. The coding scheme is used to analyze a corpus of 39 interactions between healthcare providers and patients affected by Type 2 diabetes. The exploratory quantitative analysis, for the purpose of determining the different distributions of metaphor uses between patients and providers, is combined with qualitative analysis in which the thematic areas of the metaphors are considered. The findings show how patients and providers use metaphors for pursuing different dialogical goals and meeting distinct communicative needs.

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10

Collen, M. F. "Full-text medical literature retrieval by computer. A pilot test." JAMA: The Journal of the American Medical Association 254, no. 19 (November 15, 1985): 2768–74. http://dx.doi.org/10.1001/jama.254.19.2768.

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11

Collen, Morris F. "Full-text medical literature retrieval for continuing medical education." Möbius: A Journal for Continuing Education Professionals in Health Sciences 6, no. 2 (April 1986): 40–45. http://dx.doi.org/10.1002/chp.4760060207.

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12

MNair, Revathi, and Sindhu L. "A Survey on Medical Text Mining." International Journal of Computer Applications 108, no. 15 (December 18, 2014): 5–11. http://dx.doi.org/10.5120/18985-0423.

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13

Wacha, Richard S., Ruth Leventhal, and Russell F. Cheadle. "Medical Parasitology: A Self Instructional Text." Journal of Parasitology 76, no. 3 (June 1990): 339. http://dx.doi.org/10.2307/3282662.

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14

Smith, Andrew G. "Medical Mycology: a Self-Instructional Text." American Journal of Tropical Medicine and Hygiene 34, no. 6 (November 1, 1985): 1233–34. http://dx.doi.org/10.4269/ajtmh.1985.34.1233.

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15

Smith, Andrew G. "Medical Parasitology: A Self-Instructional Text." American Journal of Tropical Medicine and Hygiene 35, no. 1 (January 1, 1986): 212. http://dx.doi.org/10.4269/ajtmh.1986.35.1.tm0350010212a.

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16

Li, Xiao-Bai, and Jialun Qin. "Anonymizing and Sharing Medical Text Records." Information Systems Research 28, no. 2 (June 2017): 332–52. http://dx.doi.org/10.1287/isre.2016.0676.

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17

Giusfredi, Federico. "The Akkadian Medical Text KUB 37.1." Altorientalische Forschungen 39, no. 1 (December 2012): 49–63. http://dx.doi.org/10.1524/aofo.2012.0003.

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18

LEHNERT, WENDY, STEPHEN SODERLAND, DAVID ARONOW, FANGFANG FENG, and AVINOAM SHMUELI. "Inductive text classification for medical applications." Journal of Experimental & Theoretical Artificial Intelligence 7, no. 1 (January 1995): 49–80. http://dx.doi.org/10.1080/09528139508953800.

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19

Ajello, Libero. "Medical mycology. A self-instructional text." Diagnostic Microbiology and Infectious Disease 5, no. 2 (July 1986): 182–83. http://dx.doi.org/10.1016/0732-8893(86)90123-9.

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20

Monroe, Lee S. "Medical parasitology. A self-instructional text." Gastroenterology 90, no. 5 (May 1986): 1307. http://dx.doi.org/10.1016/0016-5085(86)90413-0.

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21

Paul, Simon. "Medical Law: Text, Cases, and Materials." Journal of Forensic and Legal Medicine 23 (March 2014): 94. http://dx.doi.org/10.1016/j.jflm.2013.10.008.

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22

Watson, Miss M. W. "Medical-surgical nursing—A core text." International Journal of Nursing Studies 27, no. 1 (January 1990): 94–95. http://dx.doi.org/10.1016/0020-7489(90)90027-g.

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23

Zemlevičiūtė, Palmira. "Medical Terms in a Fictional Text." Acta Linguistica Lithuanica, no. 84 (2021): 69–99. http://dx.doi.org/10.35321/all84-04.

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24

Thomann, Ursula Gubler. "Education in Occupational Therapy: The Transition to the Academic Level. Changing the Professional Identity of Occupational Therapists in Switzerland." Journal of Medical Science 88, no. 4 (December 30, 2019): 235–43. http://dx.doi.org/10.20883/medical.398.

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The aim of the article is to summarise the development of the teaching and training programme for occupational therapy in the German part of Switzerland over the years 2006-2019. As the responsible program director and project manager in the transition from higher education to an academic level, the author of this article was strongly involved in changing the professional identity of occupational therapists in Switzerland. The following text presents her personal overview of this transition. The main focus lies on education, the change process and how academisation has gradually changed the curriculum in Switzerland.

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25

Zhang, Xiao, Dejing Dou, and Ji Wu. "Learning Conceptual-Contextual Embeddings for Medical Text." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9579–86. http://dx.doi.org/10.1609/aaai.v34i05.6504.

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External knowledge is often useful for natural language understanding tasks. We introduce a contextual text representation model called Conceptual-Contextual (CC) embeddings, which incorporates structured knowledge into text representations. Unlike entity embedding methods, our approach encodes a knowledge graph into a context model. CC embeddings can be easily reused for a wide range of tasks in a similar fashion to pre-trained language models. Our model effectively encodes the huge UMLS database by leveraging semantic generalizability. Experiments on electronic health records (EHRs) and medical text processing benchmarks showed our model gives a major boost to the performance of supervised medical NLP tasks.

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26

Han, Dong Gwan. "The Critical Text from Boramae's Medical Dispute." Journal of the Korean Medical Association 47, no. 9 (2004): 814. http://dx.doi.org/10.5124/jkma.2004.47.9.814.

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27

Pereira, Luís, Rui Rijo, Catarina Silva, and Ricardo Martinho. "Text Mining Applied to Electronic Medical Records." International Journal of E-Health and Medical Communications 6, no. 3 (July 2015): 1–18. http://dx.doi.org/10.4018/ijehmc.2015070101.

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The analysis of medical records is a major challenge, considering they are generally presented in plain text, have a very specific technical vocabulary and are nearly always unstructured. It is an interdisciplinary work that requires knowledge from several fields. The analysis may have several goals, such as assistance on clinical decision, classification of medical procedures, and to support hospital management decisions. This work presents the concepts involved, the relevant existent related work, and the main open issues for future research within the analysis of electronic medical records, using data and text mining techniques. It provides a comprehensive contextualization to all those who wish to perform an analytical work of medical records, enabling the identification of fruitful research fields. With the digitalization of medical records and the large amount of medical data available, this is an area of wide research potential.

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28

Makawiti, DW. "Medical terminology: a programmed text 7th edition." Biochemical Education 24, no. 1 (January 1996): 66–67. http://dx.doi.org/10.1016/s0307-4412(96)80022-7.

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29

Filc, D. "The medical text: between biomedicine and hegemony." Social Science & Medicine 59, no. 6 (September 2004): 1275–85. http://dx.doi.org/10.1016/j.socscimed.2004.01.003.

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30

Zhan, Cheng, and Lishan Zeng. "Chinese medical interpreters’ visibility through text ownership." Interpreting. International Journal of Research and Practice in Interpreting 19, no. 1 (May 8, 2017): 97–117. http://dx.doi.org/10.1075/intp.19.1.05zha.

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The interpreter’s visibility in dialogue interpreting is a topic that has drawn extensive interest from researchers. Based on observation and recordings of 29 interpreted medical consultations at a hospital in Guangzhou, and replicating work on Spanish/English interpreting by Angelelli, this article analyzes Chinese medical interpreters’ achievement of visibility through text ownership. The dialogues, with interpreting between Chinese and English provided by four staff interpreters at the hospital, were transcribed and examined. Qualitative analysis of the transcriptions shows that the interpreters in some cases established partial or total ownership of the text and, as a result, became visible in the communication. According to how this visibility manifested itself, the medical interpreter’s text ownership can be seen as variously fulfilling four main functions: trying to expedite the drawing of conclusions; redirecting turns; expressing solidarity; and educating the patient. The research also shows that, while the purpose of a medical interpreter’s text ownership in medical encounters is to facilitate communication between the two parties to the dialogue, the visibility s/he gains by laying claim to part or all of a turn may actually prove counterproductive in this respect.

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31

Samuels, Alec. "Book Review: Medical Law: Text and Materials." Medicine, Science and the Law 30, no. 4 (October 1990): 358–59. http://dx.doi.org/10.1177/106002809003000416.

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32

Collen, Morris F. "Full-Text Medical Literature Retrieval by Computer." JAMA 254, no. 19 (November 15, 1985): 2768. http://dx.doi.org/10.1001/jama.1985.03360190074028.

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33

Boopathi. G et al. "Secure Text Sharing Using Medical Image Steganography." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 3 (March 20, 2019): 37–43. http://dx.doi.org/10.17762/ijritcc.v7i3.5259.

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The increased popularity of digital media has raised serious concerns over its security related issues. Security attacks in the form of eavesdropping, masquerading and tampering and in many other forms is very common nowadays. Data hiding is one of the emerging techniques that aim to provide for security by hiding secret information into the multimedia contents by altering some nonessential components in the host or cover file. Security of data is very important in data communication. Everyday a lot of information is transferred from one user to another on internet and so the possibility of data theft also increases. Steganography provides a solution for the security of information during data transmission. Steganography is the science which makes the valuable information invisible to prevent it from unauthorized user. A steganography system, in general, is expected to meet three key requirements, namely, imperceptibility of embedding, accurate recovery of embedded information, and large payload (payload is the bits that get delivered to the end user at the destination). So in this project an image steganography technique is proposed to hide the documents in image in the transform domain using CMD approach. The document files are carried by the image without revealing the existence to anybody. When the secret information is hidden in the carrier the result is the stego signal. In this work, the results show good quality stego signal and the stego signal is analyzed for different attacks. The stego signals are transferred to multiple receivers based on network strategies. It is found that the technique is robust and it can withstand the attacks. The quality of the stego image is measured by Peak Signal to Noise Ratio (PSNR) and other measurements.

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34

Strauss, Cyd C. "Panic Disorder: A Text for Medical Practitioners." Contemporary Psychology: A Journal of Reviews 37, no. 7 (July 1992): 655–56. http://dx.doi.org/10.1037/032329.

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35

MacKellar, B. N. "Book Review: Medical Law — Text and Materials." Medico-Legal Journal 59, no. 2 (June 1991): 137–38. http://dx.doi.org/10.1177/002581729105900220.

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36

Brahams, Diana. "Book Review: Medical Law, Text and Materials." Medico-Legal Journal 62, no. 4 (December 1994): 210–11. http://dx.doi.org/10.1177/002581729406200408.

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37

Zyga, Magdalena. "Curative or beauty treatment? Language and manipulation in leaflets of medical devices." Text & Talk 40, no. 6 (November 26, 2020): 777–97. http://dx.doi.org/10.1515/text-2020-2074.

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AbstractThe aim of the paper is to examine the mechanisms of potential (cognitive) manipulation used in the leaflets of selected medical devices. The assumption is that in the texts under analysis some of the Gricean conversational maxims are violated in an attempt at discursive manipulation or can be perceived as violated by the reader, which is favoured by activation of certain idealized cognitive models (ICMs). For the purposes of the study leaflets of four antifungal medical devices – their English- and German-language versions – are examined. The theoretical basis is mainly centred on the Gricean notion of implicature and the contemporary theory of conceptual metaphor and metonymy. The texts are analysed at relevant sub-tiers (for instance metaphor-metonymy conceptual tier) of particularly the sectional and conceptual tier. The examination of the selected leaflets reveals that the texts evoke certain ICMs whose presence seems to result – in 3 out of 4 leaflets – from, to a large extent, the intention to at least bend some Gricean maxims. Hence, the reader might be under the impression that the product does something that is not explicitly promised.

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38

Xu, Jing, Liang Gan, Mian Cheng, and Quanyuan Wu. "Unsupervised Medical Entity Recognition and Linking in Chinese Online Medical Text." Journal of Healthcare Engineering 2018 (2018): 1–13. http://dx.doi.org/10.1155/2018/2548537.

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Online medical text is full of references to medical entities (MEs), which are valuable in many applications, including medical knowledge-based (KB) construction, decision support systems, and the treatment of diseases. However, the diverse and ambiguous nature of the surface forms gives rise to a great difficulty for ME identification. Many existing solutions have focused on supervised approaches, which are often task-dependent. In other words, applying them to different kinds of corpora or identifying new entity categories requires major effort in data annotation and feature definition. In this paper, we propose unMERL, an unsupervised framework for recognizing and linking medical entities mentioned in Chinese online medical text. For ME recognition, unMERL first exploits a knowledge-driven approach to extract candidate entities from free text. Then, the categories of the candidate entities are determined using a distributed semantic-based approach. For ME linking, we propose a collaborative inference approach which takes full advantage of heterogenous entity knowledge and unstructured information in KB. Experimental results on real corpora demonstrate significant benefits compared to recent approaches with respect to both ME recognition and linking.

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39

Xiao, Wenke, Lijia Jing, Yaxin Xu, Shichao Zheng, Yanxiong Gan, and Chuanbiao Wen. "Different Data Mining Approaches Based Medical Text Data." Journal of Healthcare Engineering 2021 (December 6, 2021): 1–11. http://dx.doi.org/10.1155/2021/1285167.

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The amount of medical text data is increasing dramatically. Medical text data record the progress of medicine and imply a large amount of medical knowledge. As a natural language, they are characterized by semistructured, high-dimensional, high data volume semantics and cannot participate in arithmetic operations. Therefore, how to extract useful knowledge or information from the total available data is very important task. Using various techniques of data mining can extract valuable knowledge or information from data. In the current study, we reviewed different approaches to apply for medical text data mining. The advantages and shortcomings for each technique compared to different processes of medical text data were analyzed. We also explored the applications of algorithms for providing insights to the users and enabling them to use the resources for the specific challenges in medical text data. Further, the main challenges in medical text data mining were discussed. Findings of this paper are benefit for helping the researchers to choose the reasonable techniques for mining medical text data and presenting the main challenges to them in medical text data mining.

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40

Miller, Susan M., Ying Geng, Robert Z. Zheng, and Aaron Dewald. "Presentation of Complex Medical Information." International Journal of Cyber Behavior, Psychology and Learning 2, no. 1 (January 2012): 42–53. http://dx.doi.org/10.4018/ijcbpl.2012010104.

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The purpose was to test the effect of placement of concept maps on learning complex medical information presented online. Blocked by a median split of scores on the Paper Folding Test (Ekstrom, French, & Harman, 1979), college students were randomly assigned to one of three experimental conditions (map before text, map after text, and no map). For purposes of analyses, learners were categorized into low and high ability groups using the lower and upper 25% of scores. A 3 X 2 MANOVA was performed on two correlated dependent variables, recall and application, revealing a statistically significant interaction effect on application learning. For this dependent variable, low spatial ability learners in the no map condition (control group) scored statistically significantly lower than high spatial ability learners.

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41

Abdollahi, Mahdi, Xiaoying Gao, Yi Mei, Shameek Ghosh, Jinyan Li, and Michael Narag. "Substituting clinical features using synthetic medical phrases: Medical text data augmentation techniques." Artificial Intelligence in Medicine 120 (October 2021): 102167. http://dx.doi.org/10.1016/j.artmed.2021.102167.

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42

Venkataraman, Guhan Ram, Arturo Lopez Pineda, Oliver J. Bear Don’t Walk IV, Ashley M. Zehnder, Sandeep Ayyar, Rodney L. Page, Carlos D. Bustamante, and Manuel A. Rivas. "FasTag: Automatic text classification of unstructured medical narratives." PLOS ONE 15, no. 6 (June 22, 2020): e0234647. http://dx.doi.org/10.1371/journal.pone.0234647.

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43

Gobbel, G. T., J. Garvin, R. Reeves, R. M. Cronin, J. Heavirland, J. Williams, A. Weaver, et al. "Assisted annotation of medical free text using RapTAT." Journal of the American Medical Informatics Association 21, no. 5 (September 1, 2014): 833–41. http://dx.doi.org/10.1136/amiajnl-2013-002255.

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44

Hwang, Kyung Hoon, Haejun Lee, Geon Koh, Seog Gyun Kim, Yong Han Sun, and Duckjoo Choi. "Recent Development in Text-based Medical Image Retrieval." Journal of Biomedical Engineering Research 36, no. 3 (June 30, 2015): 55–60. http://dx.doi.org/10.9718/jber.2015.36.3.55.

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45

Xie, Qubo, Ke Zhou, Xiao Fu, and Xiaohu Fan. "Feature attention based detection model for medical text." Journal of Intelligent & Fuzzy Systems 37, no. 4 (October 25, 2019): 4585–94. http://dx.doi.org/10.3233/jifs-179292.

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46

Zingmond, David, and Leslie A. Lenert. "Monitoring Free-Text Data Using Medical Language Processing." Computers and Biomedical Research 26, no. 5 (October 1993): 467–81. http://dx.doi.org/10.1006/cbmr.1993.1033.

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47

Milieva, D. "A textbook of text linguistics for medical students." Scripta Scientifica Medica 40, no. 1 (April 20, 2008): 91. http://dx.doi.org/10.14748/ssm.v40i1.562.

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48

Gabrieli, Elmer R., and David J. Speth. "Automated analysis of medical text. II. Cognitive strategy." Journal of Medical Systems 15, no. 1 (February 1991): 65–78. http://dx.doi.org/10.1007/bf00993881.

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49

Gabrieli, Elmer R., and David J. Speth. "Automated analysis of medical text I. Clue gathering." Journal of Medical Systems 14, no. 1-2 (April 1990): 71–91. http://dx.doi.org/10.1007/bf00995882.

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50

Moore, G. William, U. N. Riede, Richard A. Polacsek, Robert E. Miller, and Grover M. Hutchins. "Automated translation of German to English medical text." American Journal of Medicine 81, no. 1 (July 1986): 103–11. http://dx.doi.org/10.1016/0002-9343(86)90190-7.

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