Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data.
Short-term Mortality Prediction for Elderly Patients Using Medicare Claims Data.
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DOI：
10.7763/ijmlc.2015.v5.506
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发表时间：
2015-06
期刊：
影响因子：
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通讯作者：
Obermeyer Z
中科院分区：
文献类型：
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作者：
Makar M;Ghassemi M;Cutler DM;Obermeyer Z
风险预测是临床医学和公共卫生的核心。虽然已经开发了许多机器学习模型来预测死亡率，但它们很少应用于临床文献，其中分类任务通常依赖于逻辑回归。其中一个原因是，现有的机器学习模型通常会通过合并数据库中不存在的特征来优化预测，这些特征对提供者和政策制定者来说是现成的，这限制了通用性和实施。在这里，我们测试了许多机器学习分类器，用于预测老年医疗保险受益人人群的6个月死亡率，使用的是大多数医疗保健支付者和提供者都可以使用的行政索赔数据库。我们表明，机器学习分类器在很大程度上优于当前广泛使用的风险预测方法，但只有在与为本研究开发的包含临床医学见解的改进特征集一起使用时。我们的工作适用于支持患者和提供者在生命结束时做出决策，以及以人口健康为导向的努力，以确定预后不良的高风险患者。
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.