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Study On Traditional Chinese Clinical Response Evaluation Based On HMM

Posted on:2010-09-19Degree:MasterType:Thesis
Country:ChinaCandidate:X XuFull Text:PDF
GTID:2144360275973555Subject:Computer Science and Technology
Abstract/Summary:PDF Full Text Request
Clinical evaluation of curation is important to the development of Chinese medicine,and longitudinal data is the basement of Chinese medicine clinical evaluation of curation.The research on lung cancer clinical data demonstrate it is a effective integrated analysis method,and it could evaluate the curative effect both in quality and quantity.Different from ordinary available statistical software that clinicians used,It is a concrete and systematic application in traditional Chinese clinical evaluation of curative effect.According to the steps of medical data mining,firstly,data of clinical lung cancer are standardized processed,search for data prerequisite when using HMM on the data and analyze the expression way of model.Secondly,apply the HMM's improved algorithm on open source JaHmm,learn iterately on data and get the parameter of model.Thirdly,find the differences between Chinese medicine treatment and the cooperation of Chinese and Western medicine,and get a conclusion that both Chinese medicine treatment and the cooperation of Chinese and Western medicine have advantages in different position.Moreover,the distinguishing feature of lung cancer curation is discovered according to the statistics on population percentage at some time points.Explain the state of curation between two groups according to observation probability distribution function's average score between two groups.The probability of any observations and the hidden state of any observation sets are analyzed according to observation probability distribution function matrix.Clinicians could forecast the patients' condition of future on current state according to the parameters of model that have learnt,and advise the best suitable therapy plan to patients.
Keywords/Search Tags:Hidden Markov Model, Longitudinal data analysis, Response evaluation
PDF Full Text Request
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