The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation

Alex Chan, Ioana Bica, Alihan Hüyük, Daniel Jarrett, Mihaela van der Schaar

Keywords: Decision Modelling, Imitation Learning, Inverse Reinforcement Learning, Simulation, Benchmark

Abstract: Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes. Several factors including the availability of public data, the intrinsically offline nature of the problem, and the complexity of human decision making, has meant that the mainstream development of algorithms is often geared towards optimal performance in tasks that do not necessarily translate well into the medical regime; often overlooking more niche issues commonly associated with the area. We therefore present a new benchmarking suite designed specifically for medical sequential decision making: the Medkit-Learn(ing) Environment, a publicly available Python package providing simple and easy access to high-fidelity synthetic medical data. While providing a standardised way to compare algorithms in a realistic medical setting we employ a generating process that disentangles the policy and environment dynamics to allow for a range of customisations, thus enabling systematic evaluation of algorithms' robustness against specific challenges prevalent in healthcare.

URL: https://github.com/XanderJC/medkit-learn