MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes.
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 docume...
| Publicado en: | Drug Safety Vol. 42; no. 1; pp. 123 - 134 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| 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=134584692&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134584692 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01145916 C5E jtl: Drug Safety issn: 01145916 maglogo: N pubinfo: dt: Jan2019 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134584692 134584692 NLM30600484 134584692 10.1007/s40264-018-0761-0 NLM30600484 134584692 ppf: 123 ppct: 11 formats: tig: atl: MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes. aug: au: Yang, Xi Bian, Jiang Gong, Yan Hogan, William R. Wu, Yonghui affil: Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA sug: subj: Adverse Drug Event Epidemiology Natural Language Processing Human Adverse Drug Event Validation Studies Comparative Studies Evaluation Research Multicenter Studies Scales Short Portable Mental Status Questionnaire Funding Source ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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