2022-12-31-huang22a.md

abstract	booktitle	title	volume	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	genre	issued	pdf	extras
Sepsis is the leading cause of death in intensive care units. It is challenging to treat sepsis because the optimal treatment is still unclear, and individual patients respond differently to treatments. Recent attempts to use reinforcement learning to provide real-time personalized treatment recommendations have shown promising results. However, the discrete action design (i.e., discretizing the continuum of action space into coarse-grained decisions) poses problems in policy learning and evaluation, and limits the effectiveness of the treatment recommendations. In this work, we proposed a continuous state and action space solution inspired by the Deep Deterministic Policy Gradient (DDPG) algorithm. We performed qualitative evaluations and applied the direct method for off-policy evaluations. Our results match clinician performance and are more clinically reasonable and explainable than the state of the art.	Proceedings of the 7th Machine Learning for Healthcare Conference	Reinforcement Learning For Sepsis Treatment: A Continuous Action Space Solution	182	2022	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	huang22a	0	Reinforcement Learning For Sepsis Treatment: A Continuous Action Space Solution	631	647	631-647	631	false	Huang, Yong and Cao, Rui and Rahmani, Amir	given family Yong Huang given family Rui Cao given family Amir Rahmani	2022-12-31		Proceedings of the 7th Machine Learning for Healthcare Conference	inproceedings	date-parts 2022 12 31	https://proceedings.mlr.press/v182/huang22a/huang22a.pdf