Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.
Objective: Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related e...
| Published in: | Journal of the American Medical Informatics Association Vol. 27; no. 1; pp. 39 - 47 |
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| Main Authors: | , , , , |
| Format: | research Journal Article |
| Published: |
Oxford University Press / USA
Jan2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141218435&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141218435 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10675027 FZ9 jtl: Journal of the American Medical Informatics Association issn: 10675027 maglogo: N pubinfo: dt: Jan2020 vid: 27 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 141218435 141218435 NLM31390003 141218435 10.1093/jamia/ocz101 NLM31390003 141218435 ppf: 39 ppct: 8 formats: tig: atl: Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods. aug: au: Christopoulou, Fenia Tran, Thy Thy Sahu, Sunil Kumar Miwa, Makoto Ananiadou, Sophia affil: National Centre for Text Mining, School of Computer Science, The University of Manchester, Manchester, United Kingdom sug: subj: Information Retrieval Methods Adverse Drug Event Natural Language Processing Drug Interactions Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Short Portable Mental Status Questionnaire ab: Objective: Identification of drugs, associated medication entities, and interactions among them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events. This article describes our participation to the n2c2 shared-task in extracting relations between medication-related entities in electronic health records.Materials and Methods: We proposed an ensemble approach for relation extraction and classification between drugs and medication-related entities. We incorporated state-of-the-art named-entity recognition (NER) models based on bidirectional long short-term memory (BiLSTM) networks and conditional random fields (CRF) for end-to-end extraction. We additionally developed separate models for intra- and inter-sentence relation extraction and combined them using an ensemble method. The intra-sentence models rely on bidirectional long short-term memory networks and attention mechanisms and are able to capture dependencies between multiple related pairs in the same sentence. For the inter-sentence relations, we adopted a neural architecture that utilizes the Transformer network to improve performance in longer sequences.Results: Our team ranked third with a micro-averaged F1 score of 94.72% and 87.65% for relation and end-to-end relation extraction, respectively (Tracks 2 and 3). Our ensemble effectively takes advantages from our proposed models. Analysis of the reported results indicated that our proposed approach is more generalizable than the top-performing system, which employs additional training data- and corpus-driven processing techniques.Conclusions: We proposed a relation extraction system to identify relations between drugs and medication-related entities. The proposed approach is independent of external syntactic tools. Analysis showed that by using latent Drug-Drug interactions we were able to significantly improve the performance of non-Drug-Drug pairs in EHRs. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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