Parkinson's Disease Prediction Based on Multistate Markov Models
800x600 In the real medical world, there are many symptoms or chronic diseases that cannot be characterized in a deterministic way, and which must be examined in a random way. In the study of these stochastic processes, Markov chains are used. There is a wide variety of phenomena that suggest a behavior in a Markov process manner such as: the probability that a patient's health to improve, to get worse, to remain stable or to progress to death within a certain time slot, depending on what happened in the previous time window. Our goal is to show that the Markov chains can be applied to the patients with Parkinson's disease in order to predict the evolution of the disease over time. So the doctor may decide a therapeutic solution that is adapted to the patient's needs, and that can improve the quality of the patient's life with Parkinson's disease in terminal stage. Normal 0 false false false EN US X NONE X NONE MicrosoftInternetExplorer4 Style Definitions table.MsoNormalTable {mso style name:"Table Normal" mso tstyle rowband size:0; mso tstyle colband size:0; mso style noshow:yes; mso style priority:99; mso style parent:" mso padding alt:0cm 5.4pt 0cm 5.4pt; mso para margin:0cm; mso para margin bottom:.0001pt; mso pagination:widow orphan; font size:10.0pt; font family:"Times New Roman""serif"
Int. J. Comput. Commun. Control