Parkinson’s Disease Prediction Based on Multistate Markov Models
نویسندگان
چکیده
منابع مشابه
Parkinson’s Disease Prediction based on Multistate Markov Models
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...
متن کاملMultistate Markov models for disease progression with classification error
Many chronic diseases have a natural interpretation in terms of staged progression. Multistate models based on Markov processes are a well-established method of estimating rates of transition between stages of disease. However, diagnoses of disease stages are sometimes subject to error. The paper presents a general hidden Markov model for simultaneously estimating transition rates and probabili...
متن کاملMultistate Markov Models for Analyzing Incomplete Life History Data
PAl-LIEN CHEN. Multistate Markov Models for Ana.!yzing Incomplete Life History Data. (Under the direction of Dr. Pranab Kumar Sen.) In most follow-up studies, there may be several types of events or states that characterize individuals experience, and their transition or occurrence rates are of interest. However, owing to the periodic nature of the observations, the information of the transitio...
متن کاملOptimal Dimensionality Reduction of Multistate Kinetic and Markov-State Models
We develop a systematic procedure for obtaining rate and transition matrices that optimally describe the dynamics of aggregated superstates formed by combining (clustering or lumping) microstates. These reduced dynamical models are constructed by matching the time-dependent occupancy-number correlation functions of the superstates in the full and aggregated systems. Identical results are obtain...
متن کاملOn Prediction Using Variable Order Markov Models
This paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. The class of such algorithms is large and in principle includes any lossless compression algorithm. We focus on six prominent prediction algorithms, including Context Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic Suffix Trees (PSTs)...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Computers Communications & Control
سال: 2013
ISSN: 1841-9844,1841-9836
DOI: 10.15837/ijccc.2013.4.498