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.

该研究提出了利用来自公开可用的激痛医学信息库（MIMIC-III）数据集的四个临床预测基准，旨在解决机器学习在医疗保健研究中缺乏公开可用基准数据集的问题。

临床时间序列数据的多任务学习和基准测试