Development of a Reinforcement Learning Algorithm to Optimize Corticosteroid Therapy in Critically Ill Patients with Sepsis.

ABSTRACT:

Background

The optimal indication, dose, and timing of corticosteroids in sepsis is controversial. Here, we used reinforcement learning to derive the optimal steroid policy in septic patients based on data on 3051 ICU admissions from the AmsterdamUMCdb intensive care database.

Methods

We identified septic patients according to the 2016 consensus definition. An actor-critic RL algorithm using ICU mortality as a reward signal was developed to determine the optimal treatment policy from time-series data on 277 clinical parameters. We performed off-policy evaluation and testing in independent subsets to assess the algorithm's performance.

Results

Agreement between the RL agent's policy and the actual documented treatment reached 59%. Our RL agent's treatment policy was mo

SUBMITTER: Bologheanu R 

PROVIDER: S-EPMC9961939 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature