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

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

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Article ID: iaor20072390
Country: Netherlands
Volume: 9
Issue: 4
Start Page Number: 341
End Page Number: 348
Publication Date: Nov 2006
Journal: Health Care Management Science
Authors: , , ,
Keywords: queues: applications
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).

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