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Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients

Author

Listed:
  • Wojtek Michalowski
  • Szymon Wilk
  • Anthony Thijssen
  • Mingmei Li

Abstract

A clinical pathway implements best medical practices and represents sequencing and timing of interventions by clinicians for a particular clinical presentation. We used a Bayesian belief network (BBN) to model a clinical pathway for radical prostatectomy and to categorize patient’s length of stay (LOS) as being met or delayed given the patient’s outcomes and activities. A BBN model constructed from historical data collected as part of a retrospective chart study represents probabilistic dependencies between specific events from the pathway and identifies events directly affecting LOS. Preliminary evaluation of a BBN model on an independent test sample of patients’ data shows that model reliably categorizes LOS for the second and third day after the surgery (with overall accuracy of 82 and 84%, respectively). Copyright Springer Science + Business Media, LLC 2006

Suggested Citation

  • Wojtek Michalowski & Szymon Wilk & Anthony Thijssen & Mingmei Li, 2006. "Using a Bayesian belief network model to categorize length of stay for radical prostatectomy patients," Health Care Management Science, Springer, vol. 9(4), pages 341-348, November.
    • Handle: RePEc:kap:hcarem:v:9:y:2006:i:4:p:341-348
    DOI: 10.1007/s10729-006-9998-8
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