Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.
As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients w...
| Publicado en: | Journal of Clinical Monitoring & Computing Vol. 27; no. 4; pp. 455 - 465 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2013
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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=104078501&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104078501 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13871307 OHC jtl: Journal of Clinical Monitoring & Computing issn: 13871307 maglogo: N pubinfo: dt: Aug2013 vid: 27 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104078501 NLM23504197 2012165904 10.1007/s10877-013-9444-7 NLM23504197 104078501 ppf: 455 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery. aug: au: Engoren, Milo Habib, Robert H Dooner, John J Schwann, Thomas A affil: Department of Anesthesiology, University of Michigan, 4172 Cardiovascular Center, 1500 E. Medical Center Drive, Ann Arbor, MI, 48109-5861, USA, engorenm@med.umich.edu. sug: subj: Coronary Artery Bypass Readmission Algorithms Pharmacokinetics Artificial Intelligence Chromosomes Coronary Arteriosclerosis Surgery Female Human Logistic Regression Male Neural Networks (Computer) Programming Languages ROC Curve Random Assignment Regression Reproducibility of Results Risk Assessment Risk Factors Software Female Male ab: As many as 14 % of patients undergoing coronary artery bypass surgery are readmitted within 30 days. Readmission is usually the result of morbidity and may lead to death. The purpose of this study is to develop and compare statistical and genetic programming models to predict readmission. Patients were divided into separate Construction and Validation populations. Using 88 variables, logistic regression, genetic programs, and artificial neural nets were used to develop predictive models. Models were first constructed and tested on the Construction populations, then validated on the Validation population. Areas under the receiver operator characteristic curves (AU ROC) were used to compare the models. Two hundred and two patients (7.6 %) in the 2,644 patient Construction group and 216 (8.0 %) of the 2,711 patient Validation group were re-admitted within 30 days of CABG surgery. Logistic regression predicted readmission with AU ROC = .675 ± .021 in the Construction group. Genetic programs significantly improved the accuracy, AU ROC = .767 ± .001, p < .001). Artificial neural nets were less accurate with AU ROC = 0.597 ± .001 in the Construction group. Predictive accuracy of all three techniques fell in the Validation group. However, the accuracy of genetic programming (AU ROC = .654 ± .001) was still trivially but statistically non-significantly better than that of the logistic regression (AU ROC = .644 ± .020, p = .61). Genetic programming and logistic regression provide alternative methods to predict readmission that are similarly accurate. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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