Parkinson's disease (PD) is one of the most common disabling neurological disorders and results in substantial burden for patients, their families and the as a whole society in terms of increased health resource use and poor quality of life. For all stages of PD, medication therapy is the preferred medical treatment. The failure of medical regimes to prevent disease progression and to prevent long-term side effects has led to a resurgence of interest in surgical procedures. Partially observable Markov decision models (POMDPs) are a powerful and appropriate technique for decision making. In this paper we applied the model of POMDP's as a supportive tool to clinical decisions for the treatment of patients with Parkinson's disease. The aim of the model was to determine the critical threshold level to perform the surgery in order to minimize the total lifetime costs over a patient's lifetime (where the costs incorporate duration of life, quality of life, and monetary units). Under some reasonable conditions reflecting the practical meaning of the deterioration and based on the various diagnostic observations we find an optimal average cost policy for patients with PD with three deterioration levels.

Objective:To assess the cost-effectiveness of two treatments (medical treatment and amputation) in patients with diabetic foot syndrome, one of the most disabling diabetic complications. Diabetes mellitus is a massive health care problem worldwide with a current prevalence of 150 millions diabetic cases, estimated to increase to 300 million cases in 2025.Methods:Integrating medical knowledge and advances into the clinical setting is often difficult due to the complexity of the algorithms and protocols. Clinical decision support systems assist the clinician in applying new information to patient care through the analysis of patient-specific clinical variables. We require strategic decision support to analyze the cost-effectiveness of these programs compared to the status quo. We provide a simple partially observable Markov model to investigate that issue, and we propose an heuristic algorithm to find the best policy of intervention.Results:This study assesses the potential cost-effectiveness of two alternative treatment interventions in patients with diabetic foot syndrome. The implementation of the heuristic algorithm solution will assist doctors in clinical decision making, and health care organizations in evaluating medication choices for effective treatment. Finally, our study reveals that treatment programs are highly cost-effective for patients at high risk of diabetic foot ulcers and lower extremity amputations.

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Prostate cancer is second only to lung cancer as the leading cause of cancer deaths in the world Furthermore, policies are difficult to make because of the generally indolent nature of prostate cancer and because it tends to occur in older men who often have multiple, competing medical illnesses. In this paper we applied a Partially observable Markov decision processes (POMDP) formulation to the problem of treating patients with Early prostate Cancer (EPC). The purpose of this paper is to address the challenge of effectively managing Early Prostate cancer therapies. To solve this problem we used a procedure that take advantage of special problem structure, and we provide optimal policies to stochastic and dynamic decisions naturally arise in finding optimal disease treatment plans.