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

By Joseph D. Futoma, Anthony L Lin, Mark P. Sendak, Armando Bedoya, Meredith Clement, Cara O'Brien, Katherine A. Heller. 2018

22 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Chun-Hao Kingsley Chang
5. Mingjie Mai
6. Anna Goldenberg
7. Guoqi Ren
8. Javier Antorán Cabiscol

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “[55] used multioutput Gaussian processes and DRL to directly learn from sparsely sampled and frequently missing multivariate time-series ICU data.”
• Dynamic Measurement Scheduling for Adverse Event Forecasting using Deep RL (2018, methodology): “Deep RL in health care Several recent works used RL to learn a treatment plan in ICU (Weng et al., 2017; Raghu et al., 2017; Futoma et al., 2018).”