Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings.
Objective: An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or from the usage of drugs. Extracting ADEs from clinical records can help physicians associate adverse events to targeted drugs.Materials and Methods: We p...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 27; no. 1; pp. 47 - 56 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Jan2020
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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=141218436&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141218436 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: 141218436 141218436 NLM31334805 141218436 10.1093/jamia/ocz120 NLM31334805 141218436 ppf: 47 ppct: 9 formats: tig: atl: Adverse drug event and medication extraction in electronic health records via a cascading architecture with different sequence labeling models and word embeddings. aug: au: Dai, Hong-Jie Su, Chu-Hsien Wu, Chi-Shin affil: Department of Electrical Engineering, College of Electrical Engineering and Computer Science, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan sug: subj: Natural Language Processing Information Retrieval Methods Adverse Drug Event Narratives Human Algorithms Nomenclature Comparative Studies Multicenter Studies Evaluation Research Validation Studies Scales Questionnaires ab: Objective: An adverse drug event (ADE) refers to an injury resulting from medical intervention related to a drug including harm caused by drugs or from the usage of drugs. Extracting ADEs from clinical records can help physicians associate adverse events to targeted drugs.Materials and Methods: We proposed a cascading architecture to recognize medical concepts including ADEs, drug names, and entities related to drugs. The architecture includes a preprocessing method and an ensemble of conditional random fields (CRFs) and neural network-based models to respectively address the challenges of surrogate string and overlapping annotation boundaries observed in the employed ADEs and medication extraction (ADME) corpus. The effectiveness of applying different pretrained and postprocessed word embeddings for the ADME task was also studied.Results: The empirical results showed that both CRFs and neural network-based models provide promising solution for the ADME task. The neural network-based models particularly outperformed CRFs in concept types involving narrative descriptions. Our best run achieved an overall micro F-score of 0.919 on the employed corpus. Our results also suggested that the Global Vectors for word representation embedding in general domain provides a very strong baseline, which can be further improved by applying the principal component analysis to generate more isotropic vectors.Conclusions: We have demonstrated that the proposed cascading architecture can handle the problem of overlapped annotations and further improve the overall recall and F-scores because the architecture enables the developed models to exploit more context information and forms an ensemble for creating a stronger recognizer. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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