A comparison of three techniques for rapid model development: an application in patient risk-stratification.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Eisenstein, EL; Alemi, F

Published Date

  • 1996

Published In

Start / End Page

  • 443 - 447

PubMed ID

  • 8947705

Pubmed Central ID

  • 8947705

International Standard Serial Number (ISSN)

  • 1091-8280


  • eng

Conference Location

  • United States