Xi Yang1, Jiang Bian1, Yan Gong2, William R Hogan1, Yonghui Wu3. 1. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA. 2. Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, College of Pharmacy, University of Florida, Gainesville, FL, USA. 3. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA. yonghui.wu@ufl.edu.
Abstract
INTRODUCTION: Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigilance and drug safety surveillance. A well-known challenge to use clinical text for detection of ADEs is that much of the detailed information is documented in a narrative manner. Clinical natural language processing (NLP) is the key technology to extract information from unstructured clinical text. OBJECTIVE: We present a machine learning-based clinical NLP system-MADEx-for detecting medications, ADEs, and their relations from clinical notes. METHODS: We developed a recurrent neural network (RNN) model using a long short-term memory (LSTM) strategy for clinical name entity recognition (NER) and compared it with baseline conditional random fields (CRFs). We also developed a modified training strategy for the RNN, which outperformed the widely used early stop strategy. For relation extraction, we compared support vector machines (SVMs) and random forests on single-sentence relations and cross-sentence relations. In addition, we developed an integrated pipeline to extract entities and relations together by combining RNNs and SVMs. RESULTS: MADEx achieved the top-three best performances (F1 score of 0.8233) for clinical NER in the 2018 Medication and Adverse Drug Events (MADE1.0) challenge. The post-challenge evaluation showed that the relation extraction module and integrated pipeline (identify entity and relation together) of MADEx are comparable with the best systems developed in this challenge. CONCLUSION: This study demonstrated the efficiency of deep learning methods for automatic extraction of medications, ADEs, and their relations from clinical text to support pharmacovigilance and drug safety surveillance.
INTRODUCTION: Early detection of adverse drug events (ADEs) from electronic health records is an important, challenging task to support pharmacovigilance and drug safety surveillance. A well-known challenge to use clinical text for detection of ADEs is that much of the detailed information is documented in a narrative manner. Clinical natural language processing (NLP) is the key technology to extract information from unstructured clinical text. OBJECTIVE: We present a machine learning-based clinical NLP system-MADEx-for detecting medications, ADEs, and their relations from clinical notes. METHODS: We developed a recurrent neural network (RNN) model using a long short-term memory (LSTM) strategy for clinical name entity recognition (NER) and compared it with baseline conditional random fields (CRFs). We also developed a modified training strategy for the RNN, which outperformed the widely used early stop strategy. For relation extraction, we compared support vector machines (SVMs) and random forests on single-sentence relations and cross-sentence relations. In addition, we developed an integrated pipeline to extract entities and relations together by combining RNNs and SVMs. RESULTS: MADEx achieved the top-three best performances (F1 score of 0.8233) for clinical NER in the 2018 Medication and Adverse Drug Events (MADE1.0) challenge. The post-challenge evaluation showed that the relation extraction module and integrated pipeline (identify entity and relation together) of MADEx are comparable with the best systems developed in this challenge. CONCLUSION: This study demonstrated the efficiency of deep learning methods for automatic extraction of medications, ADEs, and their relations from clinical text to support pharmacovigilance and drug safety surveillance.
Authors: Xi Yang; Jiang Bian; Ruogu Fang; Ragnhildur I Bjarnadottir; William R Hogan; Yonghui Wu Journal: J Am Med Inform Assoc Date: 2020-01-01 Impact factor: 4.497
Authors: Anita M Preininger; Brett South; Jeff Heiland; Adam Buchold; Mya Baca; Suwei Wang; Rex Nipper; Nawshin Kutub; Bryan Bohanan; Gretchen Purcell Jackson Journal: JAMIA Open Date: 2020-05-01