A study of deep learning approaches for medication and adverse drug event extraction from clinical text. (28th May 2019)

• Record Type: Journal Article
• Authors: Wei, Qiang; Ji, Zongcheng; Li, Zhiheng; Du, Jingcheng; Wang, Jingqi; Xu, Jun; Xiang, Yang; Tiryaki, Firat; Wu, Stephen; Zhang, Yaoyun; Tao, Cui; Xu, Hua
• Is Part Of: Journal of the American Medical Informatics Association. Volume 27:Number 1(2020)
• Page Start: 13
• Page End: 21
• Publication Date: 2019-05-28
• Subjects: adverse drug events -- electronic health records -- deep learning -- named entity recognition -- relation extraction; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocz063
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: This article presents our approaches to extraction of medications and associated adverse drug events (ADEs) from clinical documents, which is the second track of the 2018 National NLP Clinical Challenges (n2c2) shared task. Materials and Methods: The clinical corpus used in this study was from the MIMIC-III database and the organizers annotated 303 documents for training and 202 for testing. Our system consists of 2 components: a named entity recognition (NER) and a relation classification (RC) component. For each component, we implemented deep learning-based approaches (eg, BI-LSTM-CRF) and compared them with traditional machine learning approaches, namely, conditional random fields for NER and support vector machines for RC, respectively. In addition, we developed a deep learning-based joint model that recognizes ADEs and their relations to medications in 1 step using a sequence labeling approach. To further improve the performance, we also investigated different ensemble approaches to generating optimal performance by combining outputs from multiple approaches. Results: Our best-performing systems achieved F1 scores of 93.45% for NER, 96.30% for RC, and 89.05% for end-to-end evaluation, which ranked #2, #1, and #1 among all participants, respectively. Additional evaluations show that the deep learning-based approaches did outperform traditional machine learning algorithms in both NER and RC. The joint model that simultaneously recognizes ADEs and their relations to medications also achieved the best performance on RC, indicating its promise for relation extraction. Conclusion: In this study, we developed deep learning approaches for extracting medications and their attributes such as ADEs, and demonstrated its superior performance compared with traditional machine learning algorithms, indicating its uses in broader NER and RC tasks in the medical domain.