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- Title
Multi-task transfer learning for the prediction of entity modifiers in clinical text: application to opioid use disorder case detection.
- Authors
Almudaifer, Abdullateef I.; Covington, Whitney; Hairston, JaMor; Deitch, Zachary; Anand, Ankit; Carroll, Caleb M.; Crisan, Estera; Bradford, William; Walter, Lauren A.; Eaton, Ellen F.; Feldman, Sue S.; Osborne, John D.
- Abstract
Background: The semantics of entities extracted from a clinical text can be dramatically altered by modifiers, including entity negation, uncertainty, conditionality, severity, and subject. Existing models for determining modifiers of clinical entities involve regular expression or features weights that are trained independently for each modifier. Methods: We develop and evaluate a multi-task transformer architecture design where modifiers are learned and predicted jointly using the publicly available SemEval 2015 Task 14 corpus and a new Opioid Use Disorder (OUD) data set that contains modifiers shared with SemEval as well as novel modifiers specific for OUD. We evaluate the effectiveness of our multi-task learning approach versus previously published systems and assess the feasibility of transfer learning for clinical entity modifiers when only a portion of clinical modifiers are shared. Results: Our approach achieved state-of-the-art results on the ShARe corpus from SemEval 2015 Task 14, showing an increase of 1.1% on weighted accuracy, 1.7% on unweighted accuracy, and 10% on micro F1 scores. Conclusions: We show that learned weights from our shared model can be effectively transferred to a new partially matched data set, validating the use of transfer learning for clinical text modifiers.
- Subjects
OPIOID abuse; CLINICAL medicine; NATURAL language processing; TRANSFORMER models; ARCHITECTURAL design
- Publication
Journal of Biomedical Semantics, 2024, Vol 15, p1
- ISSN
2041-1480
- Publication type
Article
- DOI
10.1186/s13326-024-00311-4