Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning.
Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from...
| Publicado en: | Biomedical Informatics Insights no. 8; pp. 11 - 19 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2016
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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=118546797&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118546797 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11782226 B077 jtl: Biomedical Informatics Insights issn: 11782226 maglogo: Y pubinfo: dt: 2016 iid: 8 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 118546797 118546797 118546797 10.4137/BII.S38308 118546797 ppf: 11 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Methodological Issues in Predicting Pediatric Epilepsy Surgery Candidates Through Natural Language Processing and Machine Learning. aug: au: Bretonnel Cohen, Kevin Glass, Benjamin Greiner, Hansel M. Holland-Bouley, Katherine Standridge, Shannon Arya, Ravindra Faist, Robert Morita, Diego Mangano, Francesco Connolly, Brian Glauser, Tracy Pestian, John affil: Computational Bioscience Program, University of Colorado School of Medicine, Denver, CO, USA sug: subj: Epilepsy Surgery Natural Language Processing Learning Methods Surgical Patients Patient Classification Methods Human Child Record Review Electronic Health Records Child: 6-12 years ab: Objective: We describe the development and evaluation of a system that uses machine learning and natural language processing techniques to identify potential candidates for surgical intervention for drug-resistant pediatric epilepsy. The data are comprised of free-text clinical notes extracted from the electronic health record (EHR). Both known clinical outcomes from the EHR and manual chart annotations provide gold standards for the patient’s status. The following hypotheses are then tested: 1) machine learning methods can identify epilepsy surgery candidates as well as physicians do and 2) machine learning methods can identify candidates earlier than physicians do. These hypotheses are tested by systematically evaluating the effects of the data source, amount of training data, class balance, classification algorithm, and feature set on classifier performance. The results support both hypotheses, with F-measures ranging from 0.71 to 0.82. The feature set, classification algorithm, amount of training data, class balance, and gold standard all significantly affected classification performance. It was further observed that classification performance was better than the highest agreement between two annotators, even at one year before documented surgery referral. The results demonstrate that such machine learning methods can contribute to predicting pediatric epilepsy surgery candidates and reducing lag time to surgery referral. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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