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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(3): 192-197 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.506

Short-Term Mortality Prediction for Elderly Patients Using Medicare Claims Data

Maggie Makar, Marzyeh Ghassemi, David M. Cutler, and Ziad Obermeyer
Abstract—Risk prediction is central to both clinical medicine and public health. While many machine learning models have been developed to predict mortality, they are rarely applied in the clinical literature, where classification tasks typically rely on logistic regression. One reason for this is that existing machine learning models often seek to optimize predictions by incorporating features that are not present in the databases readily available to providers and policy makers, limiting generalizability and implementation. Here we tested a number of machine learning classifiers for prediction of six-month mortality in a population of elderly Medicare beneficiaries, using an administrative claims database of the kind available to the majority of health care payers and providers. We show that machine learning classifiers substantially outperform current widely-used methods of risk prediction—but only when used with an improved feature set incorporating insights from clinical medicine, developed for this study. Our work has applications to supporting patient and provider decision making at the end of life, as well as population health-oriented efforts to identify patients at high risk of poor outcomes.

Index Terms—Machine learning, mortality, prediction, health care.

M. Makar is with the Department of Emergency Medicine at Brigham & Women’s Hospital, Boston, USA (e-mail: mmakar@partners.org).
M. Ghassemi is with the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA (e-mail: mghassem@gmail.com).
D. Cutler is with the Department of Economics at Harvard University, Cambridge, MA, and National Bureau of Economic Research, Cambridge, USA (e-mail: dcutler@fas.harvard.edu).
Z. Obermeyer is with Department of Emergency Medicine at Harvard Medical School and the Department of Emergency Medicine at Brigham and Women’s Hospital, Boston, USA (e-mail: zobermeyer@partners.org).

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Cite: Maggie Makar, Marzyeh Ghassemi, David M. Cutler, and Ziad Obermeyer, "Short-Term Mortality Prediction for Elderly Patients Using Medicare Claims Data," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 192-197, 2015.

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