title: A Primer on Bayesian Decision Analysis With an Application to a Kidney Transplant Decision.
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 CDSS 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.
authors.affiliation: 1 Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL. 2 Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA. 3 Northwestern University Transplant Outcomes Research Collaborative, Comprehensive Transplant Center (CTC), Feinberg School of Medicine, Northwestern University, Chicago, IL. 4 School for the Science of Health Care Delivery, Arizona State University, Tempe, AZ. 5 Mayo Clinic, Scottsdale, AZ.
authors.forename: Richard
authors.initials: R
authors.lastname: Neapolitan
authors.forename: Xia
authors.initials: X
authors.lastname: Jiang
authors.forename: Daniela P
authors.initials: DP
authors.lastname: Ladner
authors.forename: Bruce
authors.initials: B
authors.lastname: Kaplan
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D003663:Decision Trees; D046148:Donor Selection / Q000379:methods*; D006801:Humans; D016030:Kidney Transplantation / Q000009:adverse effects / Q000379:methods*; D008017:Life Expectancy; D018579:Patient Selection; D018570:Risk Assessment; D012307:Risk Factors; D016896:Treatment Outcome