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Download Web Tool for Medical Decision Making Based on Markov
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Web Tool for Medical Decision Making Based on Markov Models Markov Chain The Markov Chain technique and its mathematical model have been demonstrated over years to be a powerful tool to analyse the evolution, performance and reliability of physical systems. Markov models are used to simulate both short‐cycle processes (e.g., influenza) and long‐term processes (e.g., arthritis, diabetes, heart disease) in medical decision making. Important components of a Markov model are: States: The set of distinct health states under consideration in the model, together with the possible transitions between them. Cycle length: The length of time represented by a single stage (or cycle) in a Markov process. Initial probabilities: The initial distribution of the the states Transition probabilities: The matrix of probabilities of moving between health states from one stage to the next. Rewards: Cycle costs or utilities representing the outcome measures being calculated, e.g., costs or QALYs. Markovian Assumption Behavior of the process subsequent to any cycle depends only on its description in that cycle (No memory of earlier cycles) Markov Web Tool Platform Independent Implemented in Javascript, HTML and CSS Compatible with IE9, Firefox, Chrome and Safari Works on Tablets (Android Tablets and iPad) Works on Smartphones (Android and iPhone) Validated with TreeAge pro 2009 Calculation Methods Matrix Algebraic Solution Cohort Simulation Monte Carlo Simulation Features Transition probabilities are editable State links are editable and dynamically linked with transition probability elements Provides both Cohort Analysis and Monte Carlo Analysis Comparison mechanism for two different cases (Base case vs Innovation) Provides both Markov bubble diagram and cycle tree graphical representation Four State Markov Model Example Scenario: Electrical Stimulation Therapy for Wound Healing in Diabetes Markov Bubble Diagram Markov Cycle Tree Dr. Kirusnapillai Selvarajah, Dr. Michael Craven and Prof. Steve Morgan, Faculty of Engineering www.nottingham.ac.uk/match [email protected]
