You are free to share this article under the Attribution 4.0 International license. A new method that uses deep learning to analyze vast amounts of personal health record data could identify early signs of heart failure, researchers say. A paper, which appears in the Journal of the American Medical Informatics Association (JAMIA), describes how the method addresses temporality in the data--something previously ignored by conventional machine learning models in health care applications. The research uses a deep learning model to allow earlier detection of the incidents and stages that often lead to heart failure within 6-18 months. To achieve this, researchers 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.