# Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment

arXiv: https://arxiv.org/abs/2101.03309 · HTML: https://mlsanity.com/paper/2101.03309/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 2 of its 11 references that are in the map.
- First posted on arXiv 9 Jan 2021.

## Placed in

- Offline RL and imitation learning from data / [Safe offline RL](https://mlsanity.com/offlinerl/safe-offline-rl/)

## Builds on (most cited first, 2 of 2)

- [POPCORN: Partially Observed Prediction COnstrained ReiNforcement Learning](https://mlsanity.com/paper/2001.04032/) (2020)
- [Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs](https://mlsanity.com/paper/2006.16210/) (2020)

## Built on by (most cited first, 4 of 4)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Bayesian Inverse Transition Learning for Offline Settings](https://mlsanity.com/paper/2308.05075/) (2023)
- [Learning optimal treatment strategies for intraoperative hypotension using deep reinforcement learning](https://mlsanity.com/paper/2505.21596/) (2025)
- [Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning](https://mlsanity.com/paper/2606.19481/) (2026)

## Abstract (arXiv)

Many batch RL health applications first discretize time into fixed intervals. However, this discretization both loses resolution and forces a policy computation at each (potentially fine) interval. In this work, we develop a novel framework to compress continuous trajectories into a few, interpretable decision points --places where the batch data support multiple alternatives. We apply our approach to create recommendations from a cohort of hypotensive patients dataset. Our reduced state space results in faster planning and allows easy inspection by a clinical expert.