A method for harmonization of clinical abbreviation and acronym sense inventories

Quantitative and Qualitative Analysis of Clinical Trial Acronyms From Surgical Journals

INTRODUCTION

Acronyms, the short form of a word or phrase, are commonly used in medical research to identify studies. However, their usage and quality assessment in surgical journals are unclear. This study aimed to determine the impact of identifying acronyms for clinical studies on the number of citations by comparing studies published in surgical and medical journals.

METHODS

Articles were screened from five highly cited journals (Annals of Surgery, British Journal of Surgery, JAMA Surgery, Journal of the American College of Surgeons, and New England Journal of Medicine, alphabetically). The correlation between acronym use and number of citations was analyzed. In addition, the characteristics and quality of acronyms, in terms of lettering and wording scores, used to identify studies were evaluated for acronymous trials using a developed and self-validated scoring tool.

RESULTS

Of 291 eligible articles, 167 (57.4%) were acronymous studies. Although 70.5% (122/173) of articles in general medical journals used identifying acronyms, only 38.1% (45/118) used them in surgical journals (P < 0.001). The median number of citations was higher for acronymous studies (212 versus 53; P < 0.001). Multivariable analysis revealed that acronymous studies had a 2.5-fold higher possibility of being a highly cited (odds ratio 2.514, P = 0.004). The average quality scores of the acronyms were similar for surgical and general medical journals (5.1 ± 1.7 versus 5.1 ± 1.6, P = 0.949). Surgical journals had lower lettering (2.20 ± 1.14 versus 3.02 ± 1.04, P < 0.001) but higher wording scores (2.89 ± 1.01 versus 2.09 ± 1.14, P < 0.001) than general medical journals.

CONCLUSIONS

Given the publicity effect of acronyms, a memorable acronym devised using the first or continuous letters for surgical studies may help recognize their clinical impact.

Analyzing the Creation and Use of Abbreviations in Cardiology and Cardiac Imaging Society Guidelines

Background

Abbreviation use in clinical and academic cardiology is widespread, yet there are few guidelines regulating the creation and utilization of abbreviations. Inconsistent abbreviations can introduce ambiguity and pose challenges to practice and research.

Objectives

The authors aimed to analyze how abbreviations are created and utilized in general cardiology and cardiac imaging society guidelines in order to assess whether ambiguities and discrepancies exist between societies.

Methods

Abbreviation data were collected from 7 national and international societies of general cardiology and cardiac imaging over a 6-year span (2018-2023). Data were linguistically coded for abbreviation type, unique occurrence, meaning or sense count, and frequency of discrepancy between societies.

Results

Among a total of 5,394 abbreviation tokens, there were 1,782 unique entries. Among the unique entries, 227 (12.7%) had 2 or more associated meanings (senses), and thus were potentially ambiguous. Cardiac societies differed from each other, and also internally, in their use of abbreviations, with the European Society of Cardiology representing the highest frequency of discrepant abbreviation usage (14.5%).

Conclusions

More than 12.7% of abbreviations in cardiology society guidelines had 2 or more corresponding meanings, potentially increasing the risks of miscommunication and misrepresentation. We call on cardiology and cardiac imaging societies to define and publish best practices regarding abbreviation creation and utilization.

Processing of Short-Form Content in Clinical Narratives: Systematic Scoping Review

BACKGROUND

Clinical narratives are essential components of electronic health records. The adoption of electronic health records has increased documentation time for hospital staff, leading to the use of abbreviations and acronyms more frequently. This brevity can potentially hinder comprehension for both professionals and patients.

OBJECTIVE

This review aims to provide an overview of the types of short forms found in clinical narratives, as well as the natural language processing (NLP) techniques used for their identification, expansion, and disambiguation.

METHODS

In the databases Web of Science, Embase, MEDLINE, EBMR (Evidence-Based Medicine Reviews), and ACL Anthology, publications that met the inclusion criteria were searched according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for a systematic scoping review. Original, peer-reviewed publications focusing on short-form processing in human clinical narratives were included, covering the period from January 2018 to February 2023. Short-form types were extracted, and multidimensional research methodologies were assigned to each target objective (identification, expansion, and disambiguation). NLP study recommendations and study characteristics were systematically assigned occurrence rates for evaluation.

RESULTS

Out of a total of 6639 records, only 19 articles were included in the final analysis. Rule-based approaches were predominantly used for identifying short forms, while string similarity and vector representations were applied for expansion. Embeddings and deep learning approaches were used for disambiguation.

CONCLUSIONS

The scope and types of what constitutes a clinical short form were often not explicitly defined by the authors. This lack of definition poses challenges for reproducibility and for determining whether specific methodologies are suitable for different types of short forms. Analysis of a subset of NLP recommendations for assessing quality and reproducibility revealed only partial adherence to these recommendations. Single-character abbreviations were underrepresented in studies on clinical narrative processing, as were investigations in languages other than English. Future research should focus on these 2 areas, and each paper should include descriptions of the types of content analyzed.

Sequence Labeling for Disambiguating Medical Abbreviations

Abbreviations are unavoidable yet critical parts of the medical text. Using abbreviations, especially in clinical patient notes, can save time and space, protect sensitive information, and help avoid repetitions. However, most abbreviations might have multiple senses, and the lack of a standardized mapping system makes disambiguating abbreviations a difficult and time-consuming task. The main objective of this study is to examine the feasibility of sequence labeling methods for medical abbreviation disambiguation. Specifically, we explore the capability of sequence labeling methods to deal with multiple unique abbreviations in a single text. We use two public datasets to compare and contrast the performance of several transformer models pre-trained on different scientific and medical corpora. Our proposed sequence labeling approach outperforms the more commonly used text classification models for the abbreviation disambiguation task. In particular, the SciBERT model shows a strong performance for both sequence labeling and text classification tasks over the two considered datasets. Furthermore, we find that abbreviation disambiguation performance for the text classification models becomes comparable to that of sequence labeling only when postprocessing is applied to their predictions, which involves filtering possible labels for an abbreviation based on the training data.

Language and COVID-19: A discourse analysis of resistance to lockdown in Indonesia

Communities in Indonesia were resistant to lockdown policies, Large-Scale Social Restrictions (PSBB) and the Enactment of Restrictions on Community Activities (PPKM). Both policies were implemented numerous times in the country during the COVID-19 pandemic, and these caused widespread unrest. Language with the terms PSBB and PPKM, which several times extended suddenly, not informed to the community, inconsistent in its implementation, makes the community feel mad, neglected the needs of their life, and severe rejections. This research was conducted with a qualitative approach sourced from primary and secondary data. Primary data were obtained from electronic media news that shows public resistance and government policies published through the official government web. Meanwhile, secondary data were obtained from journal articles discussing community resistance related to policies to prevent the spread of the COVID-19 pandemic. The results showed that various terms translated from the term lockdown to the time PSBB and PPKM had consequences for policy misalignment with community expectations. The switching of language from lockdown to PSBB and PPKM has caused resistance in the community because it has allowed the government to be economically irresponsible. Therefore, the government needs to inform and be responsible, so that policies can run effectively.

Disambiguating Clinical Abbreviations Using a One-Fits-All Classifier Based on Deep Learning Techniques

Background  Abbreviations are considered an essential part of the clinical narrative; they are used not only to save time and space but also to hide serious or incurable illnesses. Misreckoning interpretation of the clinical abbreviations could affect different aspects concerning patients themselves or other services like clinical support systems. There is no consensus in the scientific community to create new abbreviations, making it difficult to understand them. Disambiguate clinical abbreviations aim to predict the exact meaning of the abbreviation based on context, a crucial step in understanding clinical notes.

Objectives  Disambiguating clinical abbreviations is an essential task in information extraction from medical texts. Deep contextualized representations models showed promising results in most word sense disambiguation tasks. In this work, we propose a one-fits-all classifier to disambiguate clinical abbreviations with deep contextualized representation from pretrained language models like Bidirectional Encoder Representation from Transformers (BERT).

Methods  A set of experiments with different pretrained clinical BERT models were performed to investigate fine-tuning methods on the disambiguation of clinical abbreviations. One-fits-all classifiers were used to improve disambiguating rare clinical abbreviations.

Results  One-fits-all classifiers with deep contextualized representations from Bioclinical, BlueBERT, and MS_BERT pretrained models improved the accuracy using the University of Minnesota data set. The model achieved 98.99, 98.75, and 99.13%, respectively. All the models outperform the state-of-the-art in the previous work of around 98.39%, with the best accuracy using the MS_BERT model.

Conclusion  Deep contextualized representations via fine-tuning of pretrained language modeling proved its sufficiency on disambiguating clinical abbreviations; it could be robust for rare and unseen abbreviations and has the advantage of avoiding building a separate classifier for each abbreviation. Transfer learning can improve the development of practical abbreviation disambiguation systems.

The Unified Medical Language System at 30 Years and How It Is Used and Published: Systematic Review and Content Analysis

BACKGROUND

The Unified Medical Language System (UMLS) has been a critical tool in biomedical and health informatics, and the year 2021 marks its 30th anniversary. The UMLS brings together many broadly used vocabularies and standards in the biomedical field to facilitate interoperability among different computer systems and applications.

OBJECTIVE

Despite its longevity, there is no comprehensive publication analysis of the use of the UMLS. Thus, this review and analysis is conducted to provide an overview of the UMLS and its use in English-language peer-reviewed publications, with the objective of providing a comprehensive understanding of how the UMLS has been used in English-language peer-reviewed publications over the last 30 years.

METHODS

PubMed, ACM Digital Library, and the Nursing & Allied Health Database were used to search for studies. The primary search strategy was as follows: UMLS was used as a Medical Subject Headings term or a keyword or appeared in the title or abstract. Only English-language publications were considered. The publications were screened first, then coded and categorized iteratively, following the grounded theory. The review process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.

RESULTS

A total of 943 publications were included in the final analysis. Moreover, 32 publications were categorized into 2 categories; hence the total number of publications before duplicates are removed is 975. After analysis and categorization of the publications, UMLS was found to be used in the following emerging themes or areas (the number of publications and their respective percentages are given in parentheses): natural language processing (230/975, 23.6%), information retrieval (125/975, 12.8%), terminology study (90/975, 9.2%), ontology and modeling (80/975, 8.2%), medical subdomains (76/975, 7.8%), other language studies (53/975, 5.4%), artificial intelligence tools and applications (46/975, 4.7%), patient care (35/975, 3.6%), data mining and knowledge discovery (25/975, 2.6%), medical education (20/975, 2.1%), degree-related theses (13/975, 1.3%), digital library (5/975, 0.5%), and the UMLS itself (150/975, 15.4%), as well as the UMLS for other purposes (27/975, 2.8%).

CONCLUSIONS

The UMLS has been used successfully in patient care, medical education, digital libraries, and software development, as originally planned, as well as in degree-related theses, the building of artificial intelligence tools, data mining and knowledge discovery, foundational work in methodology, and middle layers that may lead to advanced products. Natural language processing, the UMLS itself, and information retrieval are the 3 most common themes that emerged among the included publications. The results, although largely related to academia, demonstrate that UMLS achieves its intended uses successfully, in addition to achieving uses broadly beyond its original intentions.

A deep database of medical abbreviations and acronyms for natural language processing

The recognition, disambiguation, and expansion of medical abbreviations and acronyms is of upmost importance to prevent medically-dangerous misinterpretation in natural language processing. To support recognition, disambiguation, and expansion, we present the Medical Abbreviation and Acronym Meta-Inventory, a deep database of medical abbreviations. A systematic harmonization of eight source inventories across multiple healthcare specialties and settings identified 104,057 abbreviations with 170,426 corresponding senses. Automated cross-mapping of synonymous records using state-of-the-art machine learning reduced redundancy, which simplifies future application. Additional features include semi-automated quality control to remove errors. The Meta-Inventory demonstrated high completeness or coverage of abbreviations and senses in new clinical text, a substantial improvement over the next largest repository (6–14% increase in abbreviation coverage; 28–52% increase in sense coverage). To our knowledge, the Meta-Inventory is the most complete compilation of medical abbreviations and acronyms in American English to-date. The multiple sources and high coverage support application in varied specialties and settings. This allows for cross-institutional natural language processing, which previous inventories did not support. The Meta-Inventory is available at https://bit.ly/github-clinical-abbreviations.

Measurement(s)	Controlled Vocabulary • Linguistic Form
Technology Type(s)	digital curation • data combination
Sample Characteristic - Location	United States of America

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14068949