icu-sepsis 2.0.0

Python implementation of the ICU-Sepsis environment.

The ICU-Sepsis Environment

The ICU-Sepsis environment is a reinforcement learning environment that simulates the treatment of sepsis in an intensive care unit (ICU).

Environment description

ICU-Sepsis is a tabular MDP with $N_S = 716$ states ($\{0,1,\dots,715\}$) and $N_A = 25$ actions ($\{0,1,\dots,24\}$). Each episode simulates the treatment of one sepsis patient in the ICU.

An episode ends when the patient survives (state $714$) and gets a reward of +1, or dies (state $713$) and gets a reward of 0, while all the intermediate rewards are 0. The discount factor is set as $\gamma = 1$.

Baselines

Some baseline results are shown below as a reference.

	Random	Expert	Optimal
Avg. return	0.78	0.78	0.88
Avg. episode length	9.45	9.22	10.99

The three baseline policies used are:

1. Random: Each action is taken uniformly randomly out of all the actions in any given state.
2. Expert: The estimated policy used by clinicians in the real world, computed using the data from the MIMIC-III dataset.
3. Optimal: Optimal policy computed using value iteration (requires knowledge of the transition parameters)

Installation

ICU-Sepsis can be used with Python 3.10 or later, with gymnasium 0.28.1 or later, and gym 0.21.0 or later. The environment can be installed using the pip command:

pip install icu-sepsis

Uninstallation

To uninstall, use the pip uninstall command:

pip uninstall icu_sepsis -y

Quickstart

The environment can be loaded with the Gym or Gymnasium packages and follows the standard Gym API. The following code snippet demonstrates how to create the environment, reset it, and take a step:

import gymnasium as gym
import icu_sepsis

env = gym.make('Sepsis/ICU-Sepsis-v1')

state, info = env.reset()
print('Initial state:', state)

next_state, reward, terminated, truncated, info = env.step(0)
print('Next state:', next_state)
print('Reward:', reward)
print('Terminated:', terminated)
print('Truncated:', truncated)