Deep Learning Can Now Help Prevent Heart Failure

Georgia Tech researchers are using deep learning to identify early signs of heart failure. In a paper published by the Journal of the American Medical Informatics Association (JAMIA), Georgia Tech's School of Computational Science and Engineering Associate Professor Jimeng Sun and Ph.D. student Edward Choi present a pioneering method for analyzing vast amounts of personal health record data that addresses temporality in the data – something previously ignored by conventional machine learning models in health care applications. The new research, funded by the National Institutes of Health in collaboration with Sutter Health, uses a deep learning model to enable earlier detection of the incidents and stages that often lead to heart failure within 6-18 months. To achieve this, Sun and Choi use a recurrent neural network (RNN) to model temporal relations among events in electronic health records. Temporal relationships communicate the ordering of events or states in time. This type of relation is traditionally used in natural language processing.