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An important problem in reinforcement learning is the need for greater sample efficiency. One approach to dealing with this problem is to incorporate external information elicited from a domain expert in the learning process. Indeed, it has been shown that incorporating expert advice in the learning process can improve the rate at which an agent’s...
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... The concept is to create a partnership between both machine learning capabilities and domain experts [Holzinger, 2016, Maadi et al., 2021. This "collaboration" would not only improve confidence in RL models and the recommendations they provide [Love et al., 2023] but also facilitate the utilization of this technology by healthcare professionals and patients within a clinical setting [Holzinger et al., 2019]. This fusion of machine learning and human expertise yields to improved results compared to RL in isolation or expert decisions alone [Arzate Cruz andIgarashi, 2020, Li et al., 2019a]. ...
The goal of precision medicine is to provide individualized treatment at each stage of chronic diseases, a concept formalized by Dynamic Treatment Regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness. Reinforcement Learning (RL) algorithms allow to determine these decision rules conditioned by individual patient data and their medical history. The integration of medical expertise into these models makes possible to increase confidence in treatment recommendations and facilitate the adoption of this approach by healthcare professionals and patients. In this work, we examine the mathematical foundations of RL, contextualize its application in the field of DTR, and present an overview of methods to improve its effectiveness by integrating medical expertise.
