Multitask Learning and Benchmarking with Clinical Time Series Data

Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but progress in machine learning for healthcare research has been difficult to measure because of the absence of publicly available benchmark data sets. To address this problem, we propose four clinical prediction benchmarks using data derived from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. These tasks cover a range of clinical problems including modeling risk of mortality, forecasting length of stay, detecting physiologic decline, and phenotype classification. We formulate a heterogeneous multitask problem where the goal is to jointly learn multiple clinically relevant prediction tasks based on the same time series data. To address this problem, we propose a novel recurrent neural network (RNN) architecture that leverages the correlations between the various tasks to learn a better predictive model. We validate the proposed neural architecture on this benchmark, and demonstrate that it outperforms strong baselines, including single task RNNs.