Causes of death in the United States, 1999 to 2014

摘要

Statistical methods have been widely used in studies of public health. Although useful in clinical research and public health policy making, these methods could not find correlation among health conditions automatically, or capture the temporal evolution of causes of death correctly. To cope with two challenges above, we implement the unsupervised machine learning method “topic model” to study the United States death reporting data. Our model successfully groups morbidities based on their correlation, and reveals the temporal evolution of these groups from 1999 to 2014. This result is validated by existing literature, and provides a novel view that enables clinical practitioners to make more accurate healthcare decisions, and public health policymakers to make better policy.