Learning to Treat Sepsis with Multi-Output Gaussian Process Deep Recurrent Q-Networks

no code implementations • ICLR 2018 • Joseph Futoma, Anthony Lin, Mark Sendak, Armando Bedoya, Meredith Clement, Cara O'Brien, Katherine Heller

We evaluate our approach on a heterogeneous dataset of septic spanning 15 months from our university health system, and find that our learned policy could reduce patient mortality by as much as 8. 2\% from an overall baseline mortality rate of 13. 3\%.

Gaussian Processes reinforcement-learning +3

An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis Detection

no code implementations • 19 Aug 2017 • Joseph Futoma, Sanjay Hariharan, Mark Sendak, Nathan Brajer, Meredith Clement, Armando Bedoya, Cara O'Brien, Katherine Heller

Latent function values from the Gaussian process are then fed into a deep recurrent neural network to classify patient encounters as septic or not, and the overall model is trained end-to-end using back-propagation.

Gaussian Processes Time Series Analysis