RL Based Decision Support System for u-Healthcare Environment

We can imagine a haywire situation with no healthcare centres nearby. In this situation, a high risk patient, away from the medical healthcare center, may get major heart attack or unpredictable sudden stroke, or some other noxious symptoms. Lack of on-time information, proper diagnosis, and decision making system, may sometimes cause the life of the patient. In order to access the timely information and to employ correct diagnosis at anytime and anywhere, use of ubiquitous technologies is becoming ideal test-beds for u-Healthcare environments. However, using ubiquitous device, it would be one of the most crucial requisites to accumulate accurate signals timely and appropriate processing of those signals during such critical circumstances. Furthermore, lack of proper decision support system may delay the treatment, and it may cost a life of the patient. The effort to rectify any of these issues will minimize the time lag between observation and treatment during the emergency circumstances, and helps to reduce the diagnosis time, that can be better utilize for caring the patient. The objective of this chapter is to combine the agent based decision support system with ubiquitous artefacts and make it more intelligent, so that it can help the doctors to acquire correct and timely diagnosis information and select appropriate treatment choices. Also, designed is a novel interpretation of Markov decision process, providing clear mathematical formulation to connect reinforcement learning agent system. An attempt is given to supervise the dynamic situation by using agent based ubiquitous artefacts and to find out the appropriate solution for emergency circumstances, providing correct diagnosis and proper treatment in time. The well known reinforcement learning can be utilized to model u-healthcare decision support system. The reason for using the RL (Reinforcement Learning) agent based on MDP (Markov Decision Process) model is because it needs less number of parameters compare to other decision trees it also gives approximation method to make trade off between accuracy and speed, in turn, solve the complex number of cases in less time compare to other decision support system (Milos H., Fraser H., 2000). Organization of this chapter is as follows. Section 2 is a review of the related works, RL agent, and Markov decision model is also explained. Section 3 describes the details scenario of the proposed approach. Similarly, section 4 discusses the formulation of the model and optimal policy finding algorithm of the RL based decision support system. Finally section 5 & 6 concludes the chapter and contains references.