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.

翻译：要想将机器学习的长处带来最终改善患者结果,了解临床环境中的决策至关重要。有几个因素包括公共数据的提供、问题的内在离线性质和人类决策的复杂性,这意味着,主流算法发展往往着眼于最佳地完成不一定转化为医疗制度的任务;往往忽视通常与该地区相关的更特殊的问题。因此,我们提出了一套专门为医疗连续决策设计的新的基准套件:Medkit-Learn(环)环境,这是一个公开提供的Python套件,提供简单易懂的易懂的易用综合医疗数据。虽然提供了在现实医学环境中比较算法的标准化方法,但我们采用了一种使政策和环境动态脱钩的生成过程,以便能够进行一系列定制,从而能够系统评估算法的稳健性,应对医疗保健领域普遍存在的具体挑战。