Global Experts Demonstrate
Common Machine Methods in Value in Health
Lawrenceville, NJ, USA—July 16, 2019—Value in
Health, the
official journal of ISPOR—the
professional society for health economics and outcomes research, announced today the publication
of a high-level overview of machine learning for healthcare outcomes
researchers and decision makers. The report, “Machine
Learning for Health Services Researchers,” was published in the July
2019 issue of Value in Health.
Machine learning is a
rapidly growing field that attempts to extract general concepts from large
datasets, commonly in the form of an algorithm that predicts an outcome—a task
that has become increasingly difficult to accomplish by humans because data
volume and complexity has increased beyond what was capable with traditional
statistics and desktop computers.
Machine learning methods
may be useful to health service researchers seeking to improve prediction of a
healthcare outcome with large datasets available to train and refine an
estimator algorithm. Machine learning methods can help generalizable data-driven
estimators when many covariates are being selected among and when the outcome
of interest may be produced by complex nonlinear relationships and interaction
terms.
In this report, the
authors introduce key concepts for understanding the application of machine
learning methods to healthcare outcomes research. They first provide an
overview of machine learning, then identify 5 steps to developing and applying a machine
learning algorithm (commonly referred to as a predictive model or estimator): (1)
data preparation, (2) estimator family selection, (3) estimator parameter
learning, (4) estimator regularization, and (5) estimator evaluation.
The report goes on to
compare 3 of the most common machine learning methods: (1) decision tree
methods that can be useful for identifying how different subpopulations
experience different risks for an outcome, (2) deep learning methods that can
identify complex nonlinear patterns or interactions between variables
predictive of an outcome, and (3) ensemble methods that can improve predictive
performance by combining multiple machine learning methods. Finally, the
authors demonstrate the application of common machine methods to a simulated
insurance claims dataset.
“While machine learning
methods may be useful to health service researchers, they offer considerable
challenges that are worth considering before engaging in a machine learning
activity,” said author Patrick Doupe, PhD, Zalando SE, Berlin, Germany. “Specifically,
they may be difficult to interpret (particularly for deep learning), difficult
to glean mechanistic understandings from, and may require substantial
investment of time and resources for computation. Nevertheless, improvements in
hardware and cloud computing technologies have made machine learning methods
increasingly accessible to healthcare outcomes researchers and healthcare
organizations. With
this article, we aim to lower the barriers to implementing machine learning
methods.”
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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 2018 impact factor score is 5.037. Value in Health is ranked 4th among 81 journals in health policy and services, 5th among 98 journals in healthcare sciences and services, and 11th among 363 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 (@IS