On the decision rules of cost-effective treatment for patients with diabetic foot syndrome

John E Goulionis1, Athanassios Vozikis2, VK Benos1, D Nikolakis11Department of Statistics and Insurance Science, University of Piraeus, Piraeus, Greece; 2Department of Economic Science, University of Piraeus, Piraeus, GreeceObjective: 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.Keywords: partially observable Markov decision model, diabetic foot syndrome, cost-­effectiveness metho

Medical decision making for patients with Parkinson disease under Average Cost Criterion

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

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed

AN ALGORITHM TO OBTAIN AN OPTIMAL STRATEGY FOR THE MARKOV DECISION PROCESSES, WITH PROBABILITY DISTRIBUTION FOR THE PLANNING HORIZON.

In this paper we formulate Markov Decision Processes with Random Horizon. We show the optimality equation for this problem, however there may not exist optimal stationary strategies. For the MDP (Markov�Decision�Process), with probability distribution for the planning horizon with infinite support, we show Turnpike Planning Horizon Theorem. We develop an algorithm obtaining an optimal first stage decision. We give some numerical examples.En este trabajo formulamos un Proceso de Decisión Markoviano con Horizonte Aleatorio. Desarrollamos la ecuación de optimalidad para este problema, sin embargo puede no existir estrategias optimales estacionarias. Para el MDP (Proceso de Decisión Markoviano), con distribución de probabilidad para horizonte de planeamiento con soporte infinito, demostramos el Teorema de Horizonte de Planeamiento de Turnpike. Desarrollamos un algoritmo para obtener una decisión de primera etapa optimal. Damos algunos ejemplos numéricos