Title: Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data,
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.