A primer on Bayesian decision analysis with an application to a kidney transplant decision

Richard Neapolitan*, Xia Jiang, Daniela P. Ladner, Bruce Kaplan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

A clinical decision support system (CDSS) is a computer program, which is designed to assist health care professionals with decision making tasks. A well-developed CDSS weighs the benefits of therapy versus the cost in terms of loss of quality of life and financial loss and recommends the decision that can be expected to provide maximum overall benefit. This article provides an introduction to developing CDSSs using Bayesian networks; such CDSSs can help with the often complex decisions involving transplants. First, we review Bayes theorem in the context of medical decision making. Then, we introduce Bayesian networks, which can model probabilistic relationships among many related variables and are based on Bayes theorem. Next, we discuss influence diagrams, which are Bayesian networks augmented with decision and value nodes and which can be used to develop CDSSs that are able to recommend decisions that maximize the expected utility of the predicted outcomes to the patient. By way of comparison, we examine the benefit and challenges of using the Kidney Donor Risk Index as the sole decision tool. Finally, we develop a schema for an influence diagram that models generalized kidney transplant decisions and show how the influence diagram approach can provide the clinician and the potential transplant recipient with a valuable decision support tool.

Original languageEnglish (US)
Pages (from-to)489-496
Number of pages8
JournalTransplantation
Volume100
Issue number3
DOIs
StatePublished - 2016

ASJC Scopus subject areas

  • Transplantation

Fingerprint Dive into the research topics of 'A primer on Bayesian decision analysis with an application to a kidney transplant decision'. Together they form a unique fingerprint.

Cite this