Treatment Recommendation in Critical Care: A Scalable and Interpretable Approach in Partially Observable Health States

By C. Utomo, Xue Li, Weitong Chen. 2018

11 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. Guoqi Ren
4. Shamim Nemati
5. Yinzhao Dong
6. Ruosong Wang
7. Pierre Humbert

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “d the transition model in continuous state-space, and applied two PS methods, the direct policy gradient and Proximal Policy Optimization PPO [232], to derive a treatment strategy [199]. Utomo et al. [200] proposed a graphical model that was able to show transitions of patient health conditions and ”
• Supervised-actor-critic reinforcement learning for intelligent mechanical ventilation and sedative dosing in intensive care units (2020, methodology): “[17] proposed a graphical model that was able to show transitions of patient health conditions and treatments for better explanability, and applied RL to generate a real-time treatment recommendation in ICUs.”