J Clin Monit Comput - Use of genetic programming, logistic regression, and artificial neural nets to predict readmission after coronary artery bypass surgery.

Tópicos

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Resumo

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.

Resumo Limpo

mani patient undergo coronari arteri bypass surgeri readmit within day readmiss usual result morbid may lead death purpos studi develop compar statist genet program model predict readmiss patient divid separ construct valid popul use variabl logist regress genet program artifici neural net use develop predict model model first construct test construct popul valid valid popul area receiv oper characterist curv au roc use compar model two hundr two patient patient construct group patient valid group readmit within day cabg surgeri logist regress predict readmiss au roc construct group genet program signific improv accuraci au roc p artifici neural net less accur au roc construct group predict accuraci three techniqu fell valid group howev accuraci genet program au roc still trivial statist nonsignific better logist regress au roc p genet program logist regress provid altern method predict readmiss similar accur

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