Extracting information from the text of electronic medical records to improve case detection: a systematic review.
Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of rese...
| Publicado en: | Journal of the American Medical Informatics Association Vol. 23; no. 5; pp. 1007 - 1016 |
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| Autores principales: | , , , , |
| Formato: | research systematic review Journal Article |
| Publicado: |
Oxford University Press / USA
Sep2016
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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=118964251&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 118964251 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: Sep2016 vid: 23 iid: 5 pid: 622 pub: Oxford University Press / USA artinfo: ui: 118964251 118964251 NLM26911811 118964251 10.1093/jamia/ocv180 NLM26911811 PMC4997034 [Available on 09/01/17] 118964251 ppf: 1007 ppct: 9 formats: tig: atl: Extracting information from the text of electronic medical records to improve case detection: a systematic review. aug: au: Ford, Elizabeth Carroll, John A. Smith, Helen E. Scott, Donia Cassell, Jackie A. affil: Division of Primary Care and Public Health, Brighton and Sussex Medical School, Brighton, UK sug: subj: Information Retrieval Algorithms Human Sensitivity and Specificity Natural Language Processing Diagnosis Data Mining Funding Source Systematic Review ab: Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality.Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed.Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025).Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall). pubtype: Academic Journal doctype: research systematic review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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