Active learning: a step towards automating medical concept extraction.
Objective: This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework ac...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 23; no. 2; pp. 289 - 297 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Mar2016
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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=113774236&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113774236 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: Mar2016 vid: 23 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 113774236 113774236 NLM26253132 113774236 10.1093/jamia/ocv069 NLM26253132 113774236 ppf: 289 ppct: 8 formats: tig: atl: Active learning: a step towards automating medical concept extraction. aug: au: Kholghi, Mahnoosh Sitbon, Laurianne Zuccon, Guido Nguyen, Anthony affil: Science and Engineering Faculty, Queensland University of Technology, Brisbane 4000, Queensland, Australia sug: subj: Information Retrieval Methods Problem-Based Learning Algorithms Semantics Vocabulary, Controlled Validation Studies Comparative Studies Evaluation Research Multicenter Studies Human ab: Objective: This paper presents an automatic, active learning-based system for the extraction of medical concepts from clinical free-text reports. Specifically, (1) the contribution of active learning in reducing the annotation effort and (2) the robustness of incremental active learning framework across different selection criteria and data sets are determined.Materials and Methods: The comparative performance of an active learning framework and a fully supervised approach were investigated to study how active learning reduces the annotation effort while achieving the same effectiveness as a supervised approach. Conditional random fields as the supervised method, and least confidence and information density as 2 selection criteria for active learning framework were used. The effect of incremental learning vs standard learning on the robustness of the models within the active learning framework with different selection criteria was also investigated. The following 2 clinical data sets were used for evaluation: the Informatics for Integrating Biology and the Bedside/Veteran Affairs (i2b2/VA) 2010 natural language processing challenge and the Shared Annotated Resources/Conference and Labs of the Evaluation Forum (ShARe/CLEF) 2013 eHealth Evaluation Lab.Results: The annotation effort saved by active learning to achieve the same effectiveness as supervised learning is up to 77%, 57%, and 46% of the total number of sequences, tokens, and concepts, respectively. Compared with the random sampling baseline, the saving is at least doubled.Conclusion: Incremental active learning is a promising approach for building effective and robust medical concept extraction models while significantly reducing the burden of manual annotation. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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