Geodesy

Marketing

Benchmark (surveying)

Engineering

Health care

Time series

Systems engineering

Benchmarking

Economics

Data mining

Computer science

Business

Health records

Task (project management)

Geography

Multi-task learning

Data science

Artificial intelligence

Deep learning

Economic growth

Artificial neural network

Machine learning

Scientific 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 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.

Multitask learning and benchmarking with clinical time series data