Title: Multitask Learning and Benchmarking with Clinical Time Series Data,
Abstract: 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 propose strong linear and neural baselines for
all four tasks and evaluate the effect of deep supervision, multitask training
and data-specific architectural modifications on the performance of neural
models.