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: email@example.com).
M. Ghassemi is with the Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, USA (e-mail: firstname.lastname@example.org).
D. Cutler is with the Department of Economics at Harvard University, Cambridge, MA, and National Bureau of Economic Research, Cambridge, USA (e-mail: email@example.com).
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: firstname.lastname@example.org).
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.