Bayesian Networks for Risk Prediction Using Real-World Data: A Tool for Precision Medicine

Paul Arora , Devon Boyne , Justin J. Slater , Alind Gupta , Darren R. Brenner , Marek Drużdżel


Objective The fields of medicine and public health are undergoing a data revolution. An increasing availability of data has brought about a growing interest in machine-learning algorithms. Our objective is to present the reader with an introduction to a knowledge representation and machine-learning tool for risk estimation in medical science known as Bayesian networks (BNs). Study Design In this article we review how BNs are compact and intuitive graphical representations of joint probability distributions (JPDs) that can be used to conduct causal reasoning and risk estimation analysis and offer several advantages over regression-based methods. We discuss how BNs represent a different approach to risk estimation in that they are graphical representations of JPDs that take the form of a network representing model random variables and the influences between them, respectively. Methods We explore some of the challenges associated with traditional risk prediction methods and then describe BNs, their construction, application, and advantages in risk prediction based on examples in cancer and heart disease. Results Risk modeling with BNs has advantages over regression-based approaches, and in this article we focus on three that are relevant to health outcomes research: (1) the generation of network structures in which relationships between variables can be easily communicated; (2) their ability to apply Bayes’s theorem to conduct individual-level risk estimation; and (3) their easy transformation into decision models. Conclusions Bayesian networks represent a powerful and flexible tool for the analysis of health economics and outcomes research data in the era of precision medicine.
Author Paul Arora
Paul Arora,,
, Devon Boyne
Devon Boyne,,
, Justin J. Slater
Justin J. Slater,,
, Alind Gupta
Alind Gupta,,
, Darren R. Brenner
Darren R. Brenner,,
, Marek Drużdżel (FCS / SD)
Marek Drużdżel,,
- Software Department
Journal seriesValue in Health, ISSN 1098-3015, e-ISSN 1524-4733, (N/A 140 pkt)
Issue year2019
Publication size in sheets0.5
Keywords in Englishartificial intelligence, Bayesian networks, decision models, machine learning, precision medicine, real-world data, regression-based models, risk prediction, statistical methods
ASJC Classification2719 Health Policy; 2739 Public Health, Environmental and Occupational Health
Internal identifierROC 19-20
Languageen angielski
Score (nominal)140
Score sourcejournalList
ScoreMinisterial score = 140.0, 12-02-2020, ArticleFromJournal
Publication indicators Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.648; WoS Impact Factor: 2018 = 5.037 (2) - 2018=6.131 (5)
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