Leveraging Large Language Models for Clinical Abbreviation Disambiguation.
Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but cha...
| Publicado en: | Journal of Medical Systems Vol. 48; no. 1; pp. 1 - 17 |
|---|---|
| Autores principales: | , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
2/27/2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175896399&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175896399 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: 2/27/2024 vid: 48 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175896399 175896399 175896399 10.1007/s10916-024-02049-z 175896399 ppf: 1 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Leveraging Large Language Models for Clinical Abbreviation Disambiguation. aug: au: Hosseini, Manda Hosseini, Mandana Javidan, Reza affil: Department of Computer Engineering, Zand Institute of Higher Education, Shiraz, Iran sug: subj: Language Development Models, Educational Abbreviations Information Retrieval Health Informatics Electronic Health Records ab: Clinical abbreviation disambiguation is a crucial task in the biomedical domain, as the accurate identification of the intended meanings or expansions of abbreviations in clinical texts is vital for medical information retrieval and analysis. Existing approaches have shown promising results, but challenges such as limited instances and ambiguous interpretations persist. In this paper, we propose an approach to address these challenges and enhance the performance of clinical abbreviation disambiguation. Our objective is to leverage the power of Large Language Models (LLMs) and employ a Generative Model (GM) to augment the dataset with contextually relevant instances, enabling more accurate disambiguation across diverse clinical contexts. We integrate the contextual understanding of LLMs, represented by BlueBERT and Transformers, with data augmentation using a Generative Model, called Biomedical Generative Pre-trained Transformer (BIOGPT), that is pretrained on an extensive corpus of biomedical literature to capture the intricacies of medical terminology and context. By providing the BIOGPT with relevant medical terms and sense information, we generate diverse instances of clinical text that accurately represent the intended meanings of abbreviations. We evaluate our approach on the widely recognized CASI dataset, carefully partitioned into training, validation, and test sets. The incorporation of data augmentation with the GM improves the model's performance, particularly for senses with limited instances, effectively addressing dataset imbalance and challenges posed by similar concepts. The results demonstrate the efficacy of our proposed method, showcasing the significance of LLMs and generative techniques in clinical abbreviation disambiguation. Our model achieves a good accuracy on the test set, outperforming previous methods. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|