A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries.
Objective: The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of th...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 18; no. 5; pp. 601 - 607 |
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| Autores principales: | , , , , , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2011
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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=104577234&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104577234 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: Sep2011 vid: 18 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 104577234 NLM21508414 2011241432 10.1136/amiajnl-2011-000163 NLM21508414 PMC3168315 104577234 ppf: 601 ppct: 6 formats: tig: atl: A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. aug: au: Jiang M Chen Y Liu M Rosenbloom ST Mani S Denny JC Xu H Jiang, Min Chen, Yukun Liu, Mei Rosenbloom, S Trent Mani, Subramani Denny, Joshua C Xu, Hua affil: Department of Biomedical Informatics, Vanderbilt University, School of Medicine, Nashville, Tennessee 37232, USA sug: subj: Data Mining Classification Decision Support Systems, Clinical Classification Electronic Health Records Classification Natural Language Processing Patient Discharge Information Science Artificial Intelligence Semantics Vocabulary, Controlled ab: Objective: The authors' goal was to develop and evaluate machine-learning-based approaches to extracting clinical entities-including medical problems, tests, and treatments, as well as their asserted status-from hospital discharge summaries written using natural language. This project was part of the 2010 Center of Informatics for Integrating Biology and the Bedside/Veterans Affairs (VA) natural-language-processing challenge.Design: The authors implemented a machine-learning-based named entity recognition system for clinical text and systematically evaluated the contributions of different types of features and ML algorithms, using a training corpus of 349 annotated notes. Based on the results from training data, the authors developed a novel hybrid clinical entity extraction system, which integrated heuristic rule-based modules with the ML-base named entity recognition module. The authors applied the hybrid system to the concept extraction and assertion classification tasks in the challenge and evaluated its performance using a test data set with 477 annotated notes.Measurements: Standard measures including precision, recall, and F-measure were calculated using the evaluation script provided by the Center of Informatics for Integrating Biology and the Bedside/VA challenge organizers. The overall performance for all three types of clinical entities and all six types of assertions across 477 annotated notes were considered as the primary metric in the challenge.Results and Discussion: Systematic evaluation on the training set showed that Conditional Random Fields outperformed Support Vector Machines, and semantic information from existing natural-language-processing systems largely improved performance, although contributions from different types of features varied. The authors' hybrid entity extraction system achieved a maximum overall F-score of 0.8391 for concept extraction (ranked second) and 0.9313 for assertion classification (ranked fourth, but not statistically different than the first three systems) on the test data set in the challenge. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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