Dynamic Treatment Regimen (DTR) are adaptive treatment strategies using a sequence of expert decision rules, ideally one per stage of intervention, to individualize treatments for patients. They are an important tool towards enabling personalized medicine. Attempts are already being made to tackle treatment individualization with machine learning using deep reinforcement learning for better accuracy, however, these solutions require immense amounts of data. However, a lot of use cases in medicine do not have access to large amounts of data, as gathering data is extremely costly or simply unavailable. Therefore, I will investigate the combination of expert knowledge with machine learning in a hybrid dynamic treatment regimen system to enable treatment individualization in these cases. To realize this hybrid dynamic treatment regimen 3 research objectives are defined: 1) Improve personalized treatment outcome prediction with a precision medicine-based hybrid machine learning framework; 2) Design methods to include expert knowledge into reinforcement learning to compensate for small data; 3) Make the framework and methods transferable to different use cases to reduce modeling time and data needs in new use cases. This will provide an end-to-end system that predicts treatment outcomes and suggests treatment using machine learning and expert knowledge.