Leveraging electronic health records for predictive modeling of post-surgical complications.
Hospital-specific electronic health record systems are used to inform clinical practice about best practices and quality improvements. Many surgical centers have developed deterministic clinical decision rules to discover adverse events (e.g. postoperative complications) using electronic health reco...
| Publicado en: | Statistical Methods in Medical Research Vol. 27; no. 11; pp. 3271 - 3286 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
Nov2018
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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=132458879&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132458879 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09622802 31F jtl: Statistical Methods in Medical Research issn: 09622802 maglogo: Y pubinfo: dt: Nov2018 vid: 27 iid: 11 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 132458879 132458879 NLM29298612 132458879 10.1177/0962280217696115 NLM29298612 132458879 ppf: 3271 ppct: 15 formats: tig: atl: Leveraging electronic health records for predictive modeling of post-surgical complications. aug: au: Weller, Grant B. Lovely, Jenna Larson, David W. Earnshaw, Berton A. Huebner, Marianne affil: Savvysherpa, Inc., Minneapolis, MN, USA sug: subj: Postoperative Complications ROC Curve Risk Assessment Methods Algorithms Regression Scales Human ab: Hospital-specific electronic health record systems are used to inform clinical practice about best practices and quality improvements. Many surgical centers have developed deterministic clinical decision rules to discover adverse events (e.g. postoperative complications) using electronic health record data. However, these data provide opportunities to use probabilistic methods for early prediction of adverse health events, which may be more informative than deterministic algorithms. Electronic health record data from a set of 9598 colorectal surgery cases from 2010 to 2014 were used to predict the occurrence of selected complications including surgical site infection, ileus, and bleeding. Consistent with previous studies, we find a high rate of missing values for both covariates and complication information (4-90%). Several machine learning classification methods are trained on an 80% random sample of cases and tested on a remaining holdout set. Predictive performance varies by complication, although an area under the receiver operating characteristic curve as high as 0.86 on testing data was achieved for bleeding complications, and accuracy for all complications compares favorably to existing clinical decision rules. Our results confirm that electronic health records provide opportunities for improved risk prediction of surgical complications; however, consideration of data quality and consistency standards is an important step in predictive modeling with such data. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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