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.”
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.
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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.
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