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Medical terminology for dummies
\"From the language used by doctors and veterinarians to transcriptionists, drug and equipment sales reps, and even attorneys and insurance caseworkers, every medical term has a standard pronunciation, definition, and spelling. [This book] gets students and interested readers up to speed on medical terminology fundamentals, helping them master definitions, pronunciations, and the application of terms across all medical fields\"--From the publisher.
Book
Exploration of the optimal deep learning model for english-Japanese machine translation of medical device adverse event terminology
Background
In Japan, reporting of medical device malfunctions and related health problems is mandatory, and efforts are being made to standardize terminology through the Adverse Event Terminology Collection of the Japan Federation of Medical Device Associations (JFMDA). Internationally, the Adverse Event Terminology of the International Medical Device Regulators Forum (IMDRF-AET) provides a standardized terminology collection in English. Mapping between the JFMDA terminology collection and the IMDRF-AET is critical to international harmonization. However, the process of translating the terminology collections from English to Japanese and reconciling them is done manually, resulting in high human workloads and potential inaccuracies.
Objective
The purpose of this study is to investigate the optimal machine translation model for the IMDRF-AET into Japanese for the part of a function for the automatic terminology mapping system.
Methods
English-Japanese parallel data for IMDRF-AET published by the Ministry of Health, Labor and Welfare in Japan was obtained from 50 sentences randomly extracted from the terms and their definitions. These English sentences were fed into the following machine translation models to produce Japanese translations: mBART50, m2m-100, Google Translation, Multilingual T5, GPT-3, ChatGPT, and GPT-4. The evaluations included the quantitative metrics of BiLingual Evaluation Understudy (BLEU), Character Error Rate (CER), Word Error Rate (WER), Metric for Evaluation of Translation with Explicit ORdering (METEOR), and Bidirectional Encoder Representations from Transformers (BERT) score, as well as qualitative evaluations by four experts.
Results
GPT-4 outperformed other models in both the quantitative and qualitative evaluations, with ChatGPT showing the same capability, but with lower quantitative scores, in the qualitative evaluation. Scores of other models, including mBART50 and m2m-100, lagged behind, particularly in the CER and BERT scores.
Conclusion
GPT-4’s superior performance in translating medical terminology, indicates its potential utility in improving the efficiency of the terminology mapping system.
Journal Article
Savana: A Global Information Extraction and Terminology Expansion Framework in the Medical Domain
Terminological databases constitute a fundamental source of information in the medical domain. They are used daily both by practitioners in the area, as well as in academia. Several resources of this kind are available, e.g. CIE, SnomedCT or UMLS (Unified Medical Language System). These terminological databases are of high quality due to them being the result of collaborative expert knowledge. However, they may show certain drawbacks in terms of faithfully representing the ever-changing medical domain. Therefore, systems aimed at capturing novel terminological knowledge in heterogeneous text sources, and able to include them in standard terminologies have the potential to add great value to such repositories. This paper presents, first, Savana, a Biomedical Information Extraction system which, combined with a validation phase carried out by medical practitioners, is used to populate the Spanish branch of SnomedCT with novel knowledge. Second, we describe and evaluate a system which, given a novel medical term, finds its most likely hypernym, thus becoming an enabler in the task of terminological database enrichment and expansion.
Journal Article
A-Z of public health
\"This book provides a clear and comprehensive introduction to the many definitions, theories and approaches in public health. It is an important book for students and practitioners who are interested in public health, and for those who are keen to improve it. \"-- Provided by publisher.
Book
Health, Health-Related Quality of Life, and Quality of Life: What is the Difference?
The terms health, health-related quality of life (HRQoL), and quality of life (QoL) are used interchangeably. Given that these are three key terms in the literature, their appropriate and clear use is important. This paper reviews the history and definitions of the terms and considers how they have been used. It is argued that the definitions of HRQoL in the literature are problematic because some definitions fail to distinguish between HRQoL and health or between HRQoL and QoL. Many so-called HRQoL questionnaires actually measure self-perceived health status and the use of the phrase QoL is unjustified. It is concluded that the concept of HRQoL as used now is confusing. A potential solution is to define HRQoL as the way health is empirically estimated to affect QoL or use the term to only signify the utility associated with a health state.
Journal Article
An Interoperable UMLS Terminology Service Using FHIR
The Unified Medical Language System (UMLS) is an internationally recognized medical vocabulary that enables semantic interoperability across various biomedical terminologies. To use its knowledge, the users must understand its complex knowledge structure, a structure that is not interoperable or is not compliant with any known biomedical and healthcare standard. Further, the users also need to have good technical skills to understand its inner working and interact with UMLS in general. These barriers might cause UMLS usage concerns among inter-disciplinary users in biomedical and healthcare informatics. Currently, there exists no terminology service that normalizes UMLS’s complex knowledge structure to a widely accepted interoperable healthcare standard and allows easy access to its knowledge, thus hiding its workings. The objective of this research is to design and implement a light-weight terminology service that allows easy access to UMLS knowledge structured using the fast health interoperability resources (FHIR) standard, a widely accepted interoperability healthcare standard. The developed terminology service, named UMLS FHIR, leverages FHIR resources and features, and can easily be integrated into any application to consume UMLS knowledge in the FHIR format without the need to understand UMLS’s native knowledge structure and its internal working.
Journal Article
Ukraińskie zapożyczenie kołtun w polskiej terminologii medycznej
Kołtun jako zjawisko medyczne oraz językowo- kulturowe był już przedmiotem wielu publikacji. Niniejszy artykuł dotyczy dotąd nieopisanego aspektu w rozwoju tego leksemu, tzn. jego obecności w polskiej terminologii medycznej, zwłaszcza w XIX i na początku XX wieku.Kołtun, wyraz zapożyczony w XVI stuleciu z języka ukraińskiego w znaczeniu choroby, od razu uzyskał status terminu medycznego. Jest przykładem wyrazu, który z powodów pozajęzykowych (z oficjalnej medycyny zniknęła taka jednostka chorobowa) ostatecznie traci rangę naukowego terminu. Dzięki analizie materiałów z różnych słowników (głównie medycznych) oraz z literatury medycznej, okazało się również, że rangę terminu naukowego kołtun traci znacznie później (1. połowa XX wieku) niż kołtun przestaje być przez naukowe środowisko traktowany jako jednostka chorobowa lub objaw chorobowy (2. połowa XIX wieku).
Journal Article
Major adverse cardiovascular event definitions used in observational analysis of administrative databases: a systematic review
Background
Major adverse cardiovascular events (MACE) are increasingly used as composite outcomes in randomized controlled trials (RCTs) and observational studies. However, it is unclear how observational studies most commonly define MACE in the literature when using administrative data.
Methods
We identified peer-reviewed articles published in MEDLINE and EMBASE between January 1, 2010 to October 9, 2020. Studies utilizing administrative data to assess the MACE composite outcome using International Classification of Diseases 9th or 10th Revision diagnosis codes were included. Reviews, abstracts, and studies not providing outcome code definitions were excluded. Data extracted included data source, timeframe, MACE components, code definitions, code positions, and outcome validation.
Results
A total of 920 articles were screened, 412 were retained for full-text review, and 58 were included. Only 8.6% (
n
= 5/58) matched the traditional three-point MACE RCT definition of acute myocardial infarction (AMI), stroke, or cardiovascular death. None matched four-point (+unstable angina) or five-point MACE (+unstable angina and heart failure). The most common MACE components were: AMI and stroke, 15.5% (
n
= 9/58); AMI, stroke, and all-cause death, 13.8% (
n
= 8/58); and AMI, stroke and cardiovascular death 8.6% (
n
= 5/58). Further, 67% (
n
= 39/58) did not validate outcomes or cite validation studies. Additionally, 70.7% (
n
= 41/58) did not report code positions of endpoints, 20.7% (
n
= 12/58) used the primary position, and 8.6% (
n
= 5/58) used any position.
Conclusions
Components of MACE endpoints and diagnostic codes used varied widely across observational studies. Variability in the MACE definitions used and information reported across observational studies prohibit the comparison, replication, and aggregation of findings. Studies should transparently report the administrative codes used and code positions, as well as utilize validated outcome definitions when possible.
Journal Article
