Paul Arora1, Devon Boyne2, Justin J Slater3, Alind Gupta4, Darren R Brenner5, Marek J Druzdzel6. 1. Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Lighthouse Outcomes, Toronto, ON, Canada. Electronic address: paul.arora@utoronto.ca. 2. Lighthouse Outcomes, Toronto, ON, Canada; University of Calgary Cumming School of Medicine, Calgary, AB, Canada. 3. Lighthouse Outcomes, Toronto, ON, Canada. 4. Lighthouse Outcomes, Toronto, ON, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada. 5. Department of Oncology and Community Health Sciences, University of Calgary Cumming School of Medicine, Calgary, AB, Canada. 6. School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
Abstract
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.
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.
Authors: Jason Roszik; J Jack Lee; Yi-Hung Wu; Xi Liu; Masanori Kawakami; Jonathan M Kurie; Anas Belouali; Simina M Boca; Samir Gupta; Robert A Beckman; Subha Madhavan; Ethan Dmitrovsky Journal: Cancer Res Commun Date: 2022-07-06