Markov Decision Process Modeling for Multi-stage Optimization of Intervention and Treatment Strategies in Breast Cancer

The breast cancer is a prevalent problem that undermines quality of patients' lives and causes significant impacts on psychosocial wellness. Advanced sensing provides unprecedented opportunities to develop smart cancer care. The available sensing data captured from individuals enable the extraction of information pertinent to the breast cancer conditions to construct efficient and personalized intervention and treatment strategies. This research develops a novel sequential decision-making framework to determine optimal intervention and treatment planning for breast cancer patients. We design a Markov decision process (MDP) model for both objectives of intervention and treatment costs as well as quality adjusted life years (QALYs) with the data-driven and state-dependent intervention and treatment actions. The state space is defined as a vector of age, health status, prior intervention, and treatment plans. Also, the action space includes wait, prophylactic surgery, radiation therapy, chemotherapy, and their combinations. Experimental results demonstrate that prophylactic mastectomy and chemotherapy are more effective than other intervention and treatment plans in minimizing the expected cancer cost of 25 to 60 years-old patient with in-situ stage of cancer. However, wait policy leads to an optimal quality of life for a patient with the same state. The proposed MDP framework can also be generally applicable to a variety of medical domains that entail evidence-based decision making.