Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.

Background: Electronic medical records store extensive patient data and serve as a comprehensive repository, including textual medical records like surgical and imaging reports. Their utility in clinical decision support systems is substantial, but the widespread use of ambiguous and unstandardized abbreviations in clinical documents poses challenges for natural language processing in clinical decision support systems. Efficient abbreviation disambiguation methods are needed for effective information extraction. Objective: This study aims to enhance the one-to-all (OTA) framework for clinical abbreviation expansion, which uses a single model to predict multiple abbreviation meanings. The objective is to improve OTA by developing context-candidate pairs and optimizing word embeddings in Bidirectional Encoder Representations From Transformers (BERT), evaluating the model's efficacy in expanding clinical abbreviations using real data. Methods: Three datasets were used: Medical Subject Headings Word Sense Disambiguation, University of Minnesota, and Chia-Yi Christian Hospital from Ditmanson Medical Foundation Chia-Yi Christian Hospital. Texts containing polysemous abbreviations were preprocessed and formatted for BERT. The study involved fine-tuning pretrained models, ClinicalBERT and BlueBERT, generating dataset pairs for training and testing based on Huang et al's method. Results: BlueBERT achieved macro- and microaccuracies of 95.41% and 95.16%, respectively, on the Medical Subject Headings Word Sense Disambiguation dataset. It improved macroaccuracy by 0.54%-1.53% compared to two baselines, long short-term memory and deepBioWSD with random embedding. On the University of Minnesota dataset, BlueBERT recorded macro- and microaccuracies of 98.40% and 98.22%, respectively. Against the baselines of Word2Vec + support vector machine and BioWordVec + support vector machine, BlueBERT demonstrated a macroaccuracy improvement of 2.61%-4.13%. Conclusions: This research preliminarily validated the effectiveness of the OTA method for abbreviation disambiguation in medical texts, demonstrating the potential to enhance both clinical staff efficiency and research effectiveness.

Heterogeneity in nomenclature and abbreviation usage for anti-synthetase syndrome: a scoping review.

Anti-synthetase syndrome constitutes a dynamically evolving subset of Idiopathic Inflammatory Myopathy, however, the nomenclature and abbreviations for this syndrome are plagued by heterogeneity, leading to lack of consistency in literature. The objective of this study is to evaluate existing diversity in disease names and abbreviations, with a future goal to develop consensus on the nomenclature. A scoping review format was used for analysis. A comprehensive PUBMED search was conducted from January 1, 1984 (the initial description of anti-synthetase autoantibodies) to November 30, 2023, encompassing all pertinent articles published within this timeframe. Search terms included, ((antisynthetase syndrome) OR (anti synthetase syndrome)) OR (anti-synthetase syndrome)). The articles were screened for presence of terminology and abbreviations used. The search yielded 936 items with the specified terms. After excluding 303 irrelevant articles and 58 non-English publications, the remaining n = 575 articles underwent detailed review of the abstract and full article. Out of n = 575, 54.7% (n = 314) used 'antisynthetase syndrome' and 43.4% (n = 249) preferred 'anti-synthetase syndrome' with few novel names also. Among these, 394 articles used abbreviations while 181 did not. Most utilized term was ASS; in 64.7% (n = 255), followed AS in 11.9% (n = 47), ASSD in 9.9% (n = 39) and ASyS in 7.6% (n = 30). A discordance in nomenclature is evident, with about half using antisynthetase syndrome and other half using anti-synthetase syndrome. Moreover, significant heterogeneity exists in abbreviation use aswell. There is a pressing need to bridge this disparity and establish a uniform identifier for the disease with an objective to develop greater coherence in future research, educational initiatives, and interdisciplinary collaboration.

Improving clinical abbreviation sense disambiguation using attention-based Bi-LSTM and hybrid balancing techniques in imbalanced datasets.

RATIONALE: Clinical abbreviations pose a challenge for clinical decision support systems due to their ambiguity. Additionally, clinical datasets often suffer from class imbalance, hindering the classification of such data. This imbalance leads to classifiers with low accuracy and high error rates. Traditional feature-engineered models struggle with this task, and class imbalance is a known factor that reduces the performance of neural network techniques. AIMS AND OBJECTIVES: This study proposes an attention-based bidirectional long short-term memory (Bi-LSTM) model to improve clinical abbreviation disambiguation in clinical documents. We aim to address the challenges of limited training data and class imbalance by employing data generation techniques like reverse substitution and data augmentation with synonym substitution. METHOD: We utilise a Bi-LSTM classification model with an attention mechanism to disambiguate each abbreviation. The model's performance is evaluated based on accuracy for each abbreviation. To address the limitations of imbalanced data, we employ data generation techniques to create a more balanced dataset. RESULTS: The evaluation results demonstrate that our data balancing technique significantly improves the model's accuracy by 2.08%. Furthermore, the proposed attention-based Bi-LSTM model achieves an accuracy of 96.09% on the UMN dataset, outperforming state-of-the-art results. CONCLUSION: Deep neural network methods, particularly Bi-LSTM, offer promising alternatives to traditional feature-engineered models for clinical abbreviation disambiguation. By employing data generation techniques, we can address the challenges posed by limited-resource and imbalanced clinical datasets. This approach leads to a significant improvement in model accuracy for clinical abbreviation disambiguation tasks.

Disambiguation of acronyms in clinical narratives with large language models.

OBJECTIVE: To assess the performance of large language models (LLMs) for zero-shot disambiguation of acronyms in clinical narratives. MATERIALS AND METHODS: Clinical narratives in English, German, and Portuguese were applied for testing the performance of four LLMs: GPT-3.5, GPT-4, Llama-2-7b-chat, and Llama-2-70b-chat. For English, the anonymized Clinical Abbreviation Sense Inventory (CASI, University of Minnesota) was used. For German and Portuguese, at least 500 text spans were processed. The output of LLM models, prompted with contextual information, was analyzed to compare their acronym disambiguation capability, grouped by document-level metadata, the source language, and the LLM. RESULTS: On CASI, GPT-3.5 achieved 0.91 in accuracy. GPT-4 outperformed GPT-3.5 across all datasets, reaching 0.98 in accuracy for CASI, 0.86 and 0.65 for two German datasets, and 0.88 for Portuguese. Llama models only reached 0.73 for CASI and failed severely for German and Portuguese. Across LLMs, performance decreased from English to German and Portuguese processing languages. There was no evidence that additional document-level metadata had a significant effect. CONCLUSION: For English clinical narratives, acronym resolution by GPT-4 can be recommended to improve readability of clinical text by patients and professionals. For German and Portuguese, better models are needed. Llama models, which are particularly interesting for processing sensitive content on premise, cannot yet be recommended for acronym resolution.

Importance of Understanding Minority-Serving Institutional Designations and Associated Abbreviations in Contemporary US Higher Education.

Prefixes and suffixes in pharmacy might suggest a drug class, generation, or mechanism of action. As pharmacy educators, we also use an alphabet soup of acronyms and abbreviations to describe board certifications or professional organizations and our affiliation to them. Although we may be experts in nomenclature and abbreviations related to health professions education, sometimes, we also have to remind ourselves to be humble and embrace a learning mindset relative to the abbreviations and naming conventions used more broadly in higher education and in other fields. This article discusses the use of abbreviations in minority-serving institutions as well as the financial, historical, and political implications surrounding their names and definitions. The need to appreciate institutional designations and what they represent is not unique to minority-serving institutions or solely the responsibility of personnel who lead diversity, equity, inclusion, and accessibility efforts. Appreciating what institution designations mean is a logical first step toward discovering and acting upon the possibilities to facilitate and support the success of all learners recruited into the pharmacy profession.

Medical eponyms versus acronyms: what medical terminology is most beneficial to learn? A question of goals.

Appropriate use of medical terminology is one of the core conditions for successful communication in monolingual and multilingual healthcare communities. The modern scientific language is based on the descriptive terminology. However, it is often the case that the advantages of descriptive terminology are at odds with the ability to express complex concepts in just a few words. To solve this practicality problem it is customary to coin abbreviations and acronyms preferred to traditional eponyms. Today eponyms are considered ambiguous and non-descriptive, linked to the terminology of the past. The overview of this study demonstrates that the current habit of using acronyms can increase the scientific descriptive capacity compared to eponyms. On the other hand, acronyms remain ambiguous and more ephemeral than eponyms. Furthermore, eponyms are not as descriptive as acronyms, but they still carry important information for a medical student. If you truly believe in the importance of Medical Humanities in the medical curriculum, two aspects cannot be overlooked. First, eponyms bring students closer in an almost subliminal way to the history of medicine and the non-strictly technical-scientific field of medicine. Second, medicine is a complex science applied to humans and must strive to keep the patient at the center of its interests. Patients and their families preferably ask us to use eponyms. Which terminology to choose for medical students? The teachers have the last word.

Definition, acronyms, nomenclature, and classification of angioedema (DANCE): AAAAI, ACAAI, ACARE, and APAAACI DANCE consensus.

BACKGROUND: Angioedema (AE) manifests with intermittent, localized, self-limiting swelling of the subcutaneous and/or submucosal tissue. AE is heterogeneous, can be hereditary or acquired, may occur only once or be recurrent, may exhibit wheals or not, and may be due to mast cell mediators, bradykinin, or other mechanisms. Several different taxonomic systems are currently used, making it difficult to compare the results of studies, develop multicenter collaboration, and harmonize AE treatment. OBJECTIVE: We developed a consensus on the definition, acronyms, nomenclature, and classification of AE (DANCE). METHODS: The initiative involved 91 experts from 35 countries and was endorsed by 53 scientific and medical societies, and patient organizations. A consensus was reached by online discussion and voting using the Delphi process over a period of 16 months (June 2021 to November 2022). RESULTS: The DANCE initiative resulted in an international consensus on the definition, classification, and terminology of AE. The new consensus classification features 5 types and endotypes of AE and a harmonized vocabulary of abbreviations/acronyms. CONCLUSION: The DANCE classification complements current clinical guidelines and expert consensus recommendations on the diagnostic assessment and treatment of AE. DANCE does not replace current clinical guidelines, and expert consensus algorithms and should not be misconstrued in a way that affects reimbursement of medicines prescribed by physicians using sound clinical judgment. We anticipate that this new AE taxonomy and nomenclature will harmonize and facilitate AE research and clinical studies, thereby improving patient care.

Confusing terminology used in the abbreviation of pharmacy journal names.

The lack of commonly agreed terminology in pharmacy field is highly prevalent and may have influence on the relevance and robustness of the area, especially how others see pharmacy literature. Potential consequences of this poor perception of pharmacy field by the National Library of Medicine (NLM) could be the omission of several pharmacy-related Medical Subject Headings (MeSH) or the low indexing rate of pharmacy practice journals in MEDLINE. Journal name abbreviation, under the responsibility of the NLM, is the unambiguous way to identify a journal in bibliographic references and catalogs. The present study investigated the consistency of pharmacy journal abbreviations in the NLM Catalog. For the 290 journals containing any word with the root pharm in their names, a consistent procedure for NLM title abbreviations was found for 27 of the words in journal names but not for the abbreviation "Pharm", which represented several words with very different meanings: pharmaceutical, pharmaceutics, pharmacists, and pharmacy. The use by the NLM of different abbreviation for pharmaceutical and pharmaceutics would increase journal identification clarity.

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 .

It is still PCP that can stand for Pneumocystis pneumonia: Appeal for generalized use of only one acronym.

Twenty-years ago, considering the host specificity of Pneumocystis species, the human-derived Pneumocystis, Pneumocystis carinii formae specialis hominis, was renamed Pneumocystis jirovecii. Pneumocystis carinii formae specialis carinii was finally renamed Pneumocystis carinii and kept for the species derived from Rattus norvegicus. P. jirovecii is now widely used by most authors. The PCP acronym that initially referred to "Pneumocystis cariniipneumonia" was contemporaneously redefined to stand for Pneumocystispneumonia in order to avoid changing the acronym of the name of the disease that clinicians have used for several decades. Using analysis of multidata bases on PubMed, we have noted a recent acceleration in the use of PJP for Pneumocystis jiroveciipneumonia, which may be grammatically correct but not in accordance with retaining PCP, which was proposed in the early 2000s. Through this reminder, in order to standardize the literature on P. jirovecii, we plead for the use of only one acronym, PCP. LAY SUMMARY: Through this reminder on Pneumocystis nomenclature, we plead for the use of only one acronym, PCP, the retention of which was proposed in the early 2000s, and which currently stands for Pneumocystispneumonia.

Learners' motivational response to the Science, PE, & Me! curriculum: A situational interest perspective.

BACKGROUND: The Science, PE, & Me! (SPEM) curriculum is a concept-based physical education curriculum that offers students coherent educational experiences for constructing health-related fitness knowledge through movement experiences. The purpose of this study was to evaluate students' motivational response to the SPEM curriculum from the situational interest perspective. METHODS: The study used a cluster randomized controlled design in which 30 elementary schools in one of the largest metropolitan areas in the eastern United States were randomly assigned to an experimental or comparison condition. Although all students in the 3rd, 4th, and 5th grades in the targeted schools were eligible to participate in the study, a random sample of students from the experimental (n = 1749; 15 schools) and comparison groups (n = 1985; 15 schools) provided data. Students' motivational response to the SPEM curriculum or comparison curriculum was measured using the previously validated Situational Interest Scale-Elementary. Data were analyzed using structural mean modeling. RESULTS: The results demonstrated that the experimental group (as reference group) showed significantly higher enjoyment (z = -2.01), challenge (z = -6.54), exploration (z = -12.195), novelty (z = -8.80), and attention demand (z = -7.90) than the comparison group. CONCLUSION: The findings indicate that the SPEM curriculum created a more situationally interesting context for learning than the comparison physical education curriculum.