Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives.
Objective: Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 20; no. 5; pp. 859 - 867 |
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| Autores principales: | , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2013
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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=104086800&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104086800 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: Sep2013 vid: 20 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104086800 NLM23605114 2012222031 10.1136/amiajnl-2013-001625 NLM23605114 PMC3756271 104086800 ppf: 859 ppct: 8 formats: tig: atl: Combining rules and machine learning for extraction of temporal expressions and events from clinical narratives. aug: au: Kovacevic, Aleksandar Dehghan, Azad Filannino, Michele Keane, John A Nenadic, Goran affil: Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia. sug: subj: Artificial Intelligence Electronic Health Records Information Retrieval Methods Human Natural Language Processing Time Research, Medical ab: Objective: Identification of clinical events (eg, problems, tests, treatments) and associated temporal expressions (eg, dates and times) are key tasks in extracting and managing data from electronic health records. As part of the i2b2 2012 Natural Language Processing for Clinical Data challenge, we developed and evaluated a system to automatically extract temporal expressions and events from clinical narratives. The extracted temporal expressions were additionally normalized by assigning type, value, and modifier.Materials and Methods: The system combines rule-based and machine learning approaches that rely on morphological, lexical, syntactic, semantic, and domain-specific features. Rule-based components were designed to handle the recognition and normalization of temporal expressions, while conditional random fields models were trained for event and temporal recognition.Results: The system achieved micro F scores of 90% for the extraction of temporal expressions and 87% for clinical event extraction. The normalization component for temporal expressions achieved accuracies of 84.73% (expression's type), 70.44% (value), and 82.75% (modifier).Discussion: Compared to the initial agreement between human annotators (87-89%), the system provided comparable performance for both event and temporal expression mining. While (lenient) identification of such mentions is achievable, finding the exact boundaries proved challenging.Conclusions: The system provides a state-of-the-art method that can be used to support automated identification of mentions of clinical events and temporal expressions in narratives either to support the manual review process or as a part of a large-scale processing of electronic health databases. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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