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

• Published in: JMIR medical informatics Vol. 12; p. e56955
• Main Authors: Sung, Sheng-Feng, Hu, Ya-Han, Chen, Chong-Yan
• Format: Journal Article
• Language: English
• Published: Canada JMIR Publications 01.10.2024
• Subjects: Abbreviations; Abbreviations as Topic; AI Language Models in Health Care; Algorithms; Amputation; Batch processing; Case Study; Clinical decision making; Datasets; Electronic Health Records; Hospitals; Humans; Machine Learning; Medical records; Natural Language Processing; Original Paper; Subject heading schemes; Validation studies; Word sense disambiguation; text mining; word sense disambiguation; electronic medical records; abbreviation expansion; natural language processing
• DOI: 10.2196/56955
• ISSN: 2291-9694

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. 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. 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. 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%. 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.