Corpus Of Medical Texts Research Articles

Europe's Largest Research Infrastructure for Curated Medical Data Models with Semantic Annotations.

Structural metadata from the majority of clinical studies and routine health care systems is currently not yet available to the scientific community. To provide an overview of available contents in the Portal of Medical Data Models (MDM Portal). The MDM Portal is a registered European information infrastructure for research and health care, and its contents are curated and semantically annotated by medical experts. It enables users to search, view, discuss, and download existing medical data models. The most frequent keyword is "clinical trial" (n = 18,777), and the most frequent disease-specific keyword is "breast neoplasms" (n = 1,943). Most data items are available in English (n = 545,749) and German (n = 109,267). Manually curated semantic annotations are available for 805,308 elements (554,352 items, 58,101 item groups, and 192,855 code list items), which were derived from 25,257 data models. In total, 1,609,225 Unified Medical Language System (UMLS) codes have been assigned, with 66,373 unique UMLS codes. To our knowledge, the MDM Portal constitutes Europe's largest collection of medical data models with semantically annotated elements. As such, it can be used to increase compatibility of medical datasets and can be utilized as a large expert-annotated medical text corpus for natural language processing.

Method and Models of Extraction of Knowledge from Medical Documents

The paper analyzes the problem of extracting knowledge from clinical recommendations presented in the form of semi-structured corpora of text documents in natural language, taking into account their periodic updating. The considered methods of intellectual analysis of the accumulated arrays of medical data make it possible to automate a number of tasks aimed at improving the quality of medical care due to significant decision support in the treatment process. A brief review of well-known publications has been made, highlighting approaches to automating the construction of ontologies and knowledge graphs in the problems of semantic modeling of a problem-oriented text corpus. The structural and functional organization of the system of knowledge extraction and automatic construction of an ontology and a knowledge graph of a problem-oriented corpus for a specific subject area is presented. The main stages of knowledge extraction and dynamic updating of the knowledge graph are considered: named entity extraction, semantic annotation, term and keyword extraction, topic modeling, topic identification, and relationship extraction. The formalized representation of texts was obtained using a pre-trained BERT transformer model. The automatic selection of triplets "object" - "action" - "subject" based on part-of-speech markup of the text corpus was used to construct fragments of the knowledge graph. An experiment was carried out on a corpus of medical texts on a given topic (162 documents of depersonalized case histories of patients of a pediatric center) without preliminary markup in order to test the proposed solution for extracting triplets and constructing a knowledge graph based on them. An analysis of the experimental results confirms the need for a deeper markup of the corpus of text documents to take into account the specifics of medical text documents. For an unmarked corpus of texts, the proposed solution demonstrates satisfactory performance in view of the selection of atomic fragments included in the automatically generated ontology.

Cloud-based intelligent self-diagnosis and department recommendation service using Chinese medical BERT

With the rapid development of hospital informatization and Internet medical service in recent years, most hospitals have launched online hospital appointment registration systems to remove patient queues and improve the efficiency of medical services. However, most of the patients lack professional medical knowledge and have no idea of how to choose department when registering. To instruct the patients to seek medical care and register effectively, we proposed CIDRS, an intelligent self-diagnosis and department recommendation framework based on Chinese medical Bidirectional Encoder Representations from Transformers (BERT) in the cloud computing environment. We also established a Chinese BERT model (CHMBERT) trained on a large-scale Chinese medical text corpus. This model was used to optimize self-diagnosis and department recommendation tasks. To solve the limited computing power of terminals, we deployed the proposed framework in a cloud computing environment based on container and micro-service technologies. Real-world medical datasets from hospitals were used in the experiments, and results showed that the proposed model was superior to the traditional deep learning models and other pre-trained language models in terms of performance.

Guideline-Driven Medical Decision Support Methods for Family Healthcare

Medical guidelines are effective to guide medical practice and improve therapeutic effect. Currently, medical guidelines are primarily used in medical institutions but are still not available to the public. To improve the practicality of guidelines in family practice, this paper proposes a novel approach to assisting family medical decision support using semantic technology and open data analysis. A disease-specific knowledge model is constructed using semantic guideline expressions, which provide standard care plans to the public. A medical text corpus is formed by collecting medical information from open data sources online. Via text mining and sentiment analysis of the medical text corpus, the word frequency and sentiment score of different medical procedures are calculated, which are used to provide detailed instructions for public treatment. A guideline-based knowledge model is constructed to create eczema-specific standard care plans. According to the resulting frequencies, hydrocortisone butyrate cream (HBC) elicited the most concern among hormone drugs (54%), followed by mometasone furoate cream (MFC), accounting for 28% among all hormone drugs. According to the average sentiment score, MFC is more frequently recommended than HBC. For skin care products, YMJ elicited the most concern, while Cetaphil was the most recommended. The results of the word frequency analysis and sentiment analysis are combined to provide detailed and clear recommendations to supplement standard care plans for family medical decision support. The method proposed by this paper is an important supplement and extension to in-hospital data analysis.

A Depth Evidence Score Fusion Algorithm for Chinese Medical Intelligence Question Answering System.

Question answering (QA) system is becoming the focus of the research in medical health in terms of providing fleetly accurate answers to users. Numerous traditional QA systems are faced to simple factual questions and do not obtain accurate answers for complex questions. In order to realize the intelligent QA system for disease diagnosis and treatment in medical informationization, in this paper, we propose a depth evidence score fusion algorithm for Chinese Medical Intelligent Question Answering System, which can measure the text information in many algorithmic ways and ensure that the QA system outputs accurately the optimal candidate answer. At the semantic level, a new text semantic evidence score based on Word2vec is proposed, which can calculate the semantic similarity between texts. Experimental results on the medical text corpus show that the depth evidence score fusion algorithm has better performance in the evidence-scoring module of the intelligent QA system.

Web-Based Information Infrastructure Increases the Interrater Reliability of Medical Coders: Quasi-Experimental Study.

BackgroundMedical coding is essential for standardized communication and integration of clinical data. The Unified Medical Language System by the National Library of Medicine is the largest clinical terminology system for medical coders and Natural Language Processing tools. However, the abundance of ambiguous codes leads to low rates of uniform coding among different coders.ObjectiveThe objective of our study was to measure uniform coding among different medical experts in terms of interrater reliability and analyze the effect on interrater reliability using an expert- and Web-based code suggestion system.MethodsWe conducted a quasi-experimental study in which 6 medical experts coded 602 medical items from structured quality assurance forms or free-text eligibility criteria of 20 different clinical trials. The medical item content was selected on the basis of mortality-leading diseases according to World Health Organization data. The intervention comprised using a semiautomatic code suggestion tool that is linked to a European information infrastructure providing a large medical text corpus of >300,000 medical form items with expert-assigned semantic codes. Krippendorff alpha (Kalpha) with bootstrap analysis was used for the interrater reliability analysis, and coding times were measured before and after the intervention.ResultsThe intervention improved interrater reliability in structured quality assurance form items (from Kalpha=0.50, 95% CI 0.43-0.57 to Kalpha=0.62 95% CI 0.55-0.69) and free-text eligibility criteria (from Kalpha=0.19, 95% CI 0.14-0.24 to Kalpha=0.43, 95% CI 0.37-0.50) while preserving or slightly reducing the mean coding time per item for all 6 coders. Regardless of the intervention, precoordination and structured items were associated with significantly high interrater reliability, but the proportion of items that were precoordinated significantly increased after intervention (eligibility criteria: OR 4.92, 95% CI 2.78-8.72; quality assurance: OR 1.96, 95% CI 1.19-3.25).ConclusionsThe Web-based code suggestion mechanism improved interrater reliability toward moderate or even substantial intercoder agreement. Precoordination and the use of structured versus free-text data elements are key drivers of higher interrater reliability.

Language functions and medical communication: The human body as text

AbstractThis article presents the findings of a field experiment in medical English with first-year medical students at the University of Pavia, Northern Italy. Working in groups of 8–10, the students were asked to produce a corpus of medical texts in English demonstrating how the human body is itself a meaningful text (

The embodied nature of medical concepts: image schemas and language for pain

Cognitive linguistics assumes that knowledge is both embodied and situated as far as it is acquired through our bodily interaction with the world in a specific environment (e.g. Barsalou in Lang Cogn Process 18:513-562, 2003; Connell et al. in PLoS One 7:3, 2012). Therefore, embodiment provides an explanation to the mental representation and linguistic expression of concepts. Among the first, we find multimodal conceptual structures, like image schemas, which are schematic representations of embodied experiences resulting from our conceptualization of the surrounding environment (Tercedor Sánchez et al. in J Spec Transl 18:187-205, 2012). Furthermore, the way we interact with the environment and its objects is dynamic and configures how we refer to concepts both by means of images and lexicalizations. In this article, we investigate how image schemas underlie verbal and visual representations. They both evoke concepts based on exteroception, interoception and proprioception which can be lexicalized through language. More specifically, we study (1) a multimodal corpus of medical texts to examine how image schemas lexicalize in the language of medicine to represent specialized concepts and (2) medical pictures to explore the depiction of image-schematic concepts, in order to account for the verbal and visual representation of embodied concepts. We explore the concept PAIN, a sensory and emotional experience associated with actual or potential tissue damage, using corpus analysis tools (Sketch Engine) to extract information about the lexicalization of underlying image schemas in definitions and defining contexts. Then, we use the image schemas behind medical concepts to consistently select images which depict our experience of pain and the way we understand it. Finally, such lexicalizations and visualizations will help us assess how we refer to PAIN both verbally and visually.

Online assessment of content skill levels for medical texts

Content skill levels of medical texts are essential for the comprehension (and hence utility) of medical information. A text that is too professional (i.e. high skill level) for a reader may be incomprehensible to the reader, and hence be of no value. Therefore, readers of different professional backgrounds require medical texts of different content skill levels. In this paper, we explore how content skill levels of medical texts may be assessed in an online manner without relying on any domain-dependent knowledge. We find that several assessment strategies have weaknesses, and propose an intelligent online assessment strategy OCSLA. Empirical evaluation on a medical text corpus from MedlinePlus shows that OCSLA may achieve both better and more fault-tolerant performance. The contributions are of practical significance to online medical text writing and recommendation, which are essential for heath education and promotion.

The use of verbs in research articles

The aim of the study described in this article is to examine the lexical characteristics of medical research articles in English. The article describes the results obtained from the analysis of a corpus of medical texts in which the use of verbs is studied in the different sections of the medical research article. After selecting a corpus of 30 texts and tagging them with a POS Tagger, concordances of verbs were obtained. The verbs were then classified according to their meaning in lexical domains. Results show that the lexical domains are distributed differently in each section of the article, reinforcing the rhetorical functions of each section. A proposal is also made for using these results in a more practical manner, for example to enrich an electronic manual, with information that could assist professionals and translators with the writing and translation of medical research articles.

Femmes et sida dans le discours médical : la force des images

This article analyses from a corpus of medical texts published between 1983 and 1990, the contradictory, even paradoxical way in which AIDS affecting women has been represented. The social and symbolic stakes that this subject implies have lead crystallization of a certain number of social stereotypes which influenced for several years the course of scientific questioning.

Metaphors in medical english prose: A comparative study with French and Spanish

The purpose of this work is to determine the patterns of analogy underlying medically terminologized words which carry a metaphorical status. A corpus of medical texts in English, French, and Spanish was studied; the metaphors were recorded and classified according to their analogy patterns (or underlying semantic transfer). Two broad likeness categories were found in the three languages: morphological metaphors, which refer to forms and structures (geomorphical, anatomical, zoomorphical, phytomorphical, and architectural), and physiological (or functional) metaphors, which refer to processes and functions. The results show that the patterns of analogy underlying medical metaphors are language independent and differ from those underlying nonscientific metaphors. A closer linguistic analysis of medical English metaphorical words indicates that the vast majority: (a) belong to the nominal group, (b) modify specialist nouns or adjectives, and (c) are of the nominal-compound type (an additional linguistic difficulty for NNS). Because it is well known that nontechnical vocabulary used in technical ways is a source of difficulty for NNS, pedagogical guidelines are also provided so as to encourage students to relate new vocabulary to existing knowledge structures.