Lawrenceville, NJ, USA—April 25, 2019—Value in
Health, the
official journal of ISPOR—the
professional society for health economics and outcomes research, announced
today the publication of a new report introducing Bayesian networks, a
knowledge representation and machine-learning tool for risk estimation in
medical science. The article discusses several challenges associated with
traditional risk prediction methods and then describes Bayesian networks and
their construction, application, and advantages in risk prediction based on
examples in cancer and heart disease. The paper, “Bayesian
Networks for Risk Prediction Using Real-World Data: A Tool for Precision
Medicine,” was published
in the April 2019 issue of Value
in Health.
Bayesian
networks provide a robust and flexible analytic approach to the challenge of
complex health datasets, which pose specific analytic challenges because of
missing data, large size, complexity (of relationships not only between variables
but also in the datasets themselves), changing populations, and nonlinear relationships
between exposures and outcomes. In contrast to regression-based models—the tools
historically most commonly used in clinical risk prediction analysis in
medicine—Bayesian networks are compact and intuitive graphical representations that
can be used to conduct causal reasoning and risk prediction analysis.
Relying
on the Bayesian approach to statistical inference, Bayesian networks offer
several advantages over regression-based methods:
- Explicit
representation of model structure (dependencies and independences between
variables)
- So-called
“diagnostic reasoning” (from effect to cause) is naturally implemented
- Models can be
learned from data, expert knowledge (no data), or a combination of the two
approaches
- “What-if”
scenarios can be explored to conduct individual-level risk prediction
- Extension to
decision models by incorporating decision and utility nodes
- Makes no a priori assumptions of linearity or
independence between variables
- Multiple
outcomes and exposures can efficiently be handled in a single model
“The
increasing availability of large real-world datasets has brought about a
growing interest in machine-learning algorithms for extracting knowledge from
observations and for constructing personalized risk prediction models,” said
author Paul Arora, PhD, Division of Epidemiology, Dalla Lana School of Public
Health, University of Toronto, and Lighthouse Outcomes, Toronto, Ontario, Canada.
“Bayesian networks can be embedded in real-world data sources so they can
continually learn and be updated with new information thus generating
individual-level risk prediction that is up-to-date and locally relevant.
They offer a novel approach to risk prediction
and decision analysis while maintaining a high degree of flexibility to
accommodate developments in knowledge, new therapies, database size and
complexity. We are pleased to see an
expanding body of literature demonstrating the value of these approaches to problems
in health and medicine.”
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ABOUT
ISPOR
ISPOR, the professional society for health economics
and outcomes research (HEOR), is an international, multistakeholder, nonprofit
dedicated to advancing HEOR excellence to improve decision making for health
globally. The Society is the leading source for scientific conferences,
peer-reviewed and MEDLINE®-indexed publications, good practices
guidance, education, collaboration, and tools/resources in the field.
Web: www.ispor.org | LinkedIn: www.linkedin.com/company/ispororg | Twitter: www.twitter.com/ispororg (@ISPORorg) | YouTube: www.youtube.com/ispororg | Facebook: www.facebook.com/ispororg | Instagram: www.instagram.com/ispororg
ABOUT VALUE IN HEALTH
Value in
Health (ISSN 1098-3015) is an
international, indexed journal that publishes original research and health
policy articles that advance the field of health economics and outcomes
research to help healthcare leaders make evidence-based decisions. The
journal’s 2017 impact factor score is 5.494. Value in Health is ranked 3rd
among 94 journals in healthcare sciences and services, 3rd among 79 journals in
health policy and services, and 6th among 353 journals in economics. Value
in Health is a monthly publication that circulates to more than 10,000
readers around the world.
Web: www.ispor.org/valueinhealth
| Twitter: www.twitter.com/isporjournals (@ISPORjournals)