A comparison of three techniques for rapid model development: an application in patient risk-stratification.
Accurately risk-stratifying patients is a key component of health care outcomes assessment. And, many health care organizations increasingly are relying upon automated means for assistance in making patient risk-stratification decisions. Unfortunately, the process of outcome model development, as it is currently practiced, is both time consuming and difficult. We investigated the relative abilities of three modeling techniques (logistic regression, artificial neural network (ANN), and Bayesian) to rapidly develop models for risk-stratifying patients. Our results demonstrated that all three modeling techniques perform equally well in certain situations. However, the Bayesian model with conditional independence had the best overall performance. Unfortunately, none of the models were able to achieve the degree of accuracy which would be required in a medical setting.
Eisenstein
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Outcomes Research And Assessment Group, Duke Clinical Research Institute, Duke University Medical Center, Durham, NC, USA.
Alemi
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D018806:APACHE; D001499:Bayes Theorem; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000145:classification*; D016571:Neural Networks, Computer; D012372:ROC Curve; D012306:Risk