A robust hidden semi-Markov model with application to aCGH data processing

2013 ◽  
Vol 8 (4) ◽  
pp. 427 ◽  
Author(s):  
Jiarui Ding ◽  
Sohrab Shah
Keyword(s):  
Data Processing ◽  
Markov Model ◽  
Acgh Data

scholarly journals The Summarize of Improved HMM Model

2013 ◽  
Vol 756-759 ◽  
pp. 3384-3388
Author(s):  
Fang Fang Shi ◽  
Xian Yi Cheng ◽  
Xiang Chen
Keyword(s):  
Data Processing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Statistical Learning ◽  
Basic Principle ◽  
Hidden Markov ◽  
Probability Model ◽  
Series Data

The hidden markov model is a kind of important probability model of series data processing and statistical learning and it has been successfully applied in many engineering tasks. This paper introduces the basic principle of hidden markov model firstly, and then discusses the limitations of hidden markov model, as well as the improved hidden markov model which is put forward to solve these problems.

Machine Learning Based Big Data Processing Framework for Cancer Diagnosis Using Hidden Markov Model and GM Clustering

2017 ◽  
Vol 102 (3) ◽  
pp. 2099-2116 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
V. Vijayakumar ◽  
R. Varatharajan ◽  
Priyan Malarvizhi Kumar ◽  
Revathi Sundarasekar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Processing ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Cancer Diagnosis ◽  
Hidden Markov ◽  
Big Data Processing ◽  
Processing Framework

The Karolinska Hospital Information System

1974 ◽  
Vol 13 (03) ◽  
pp. 125-140 ◽  
Author(s):  
Ch. Mellner ◽  
H. Selajstder ◽  
J. Wolodakski
Keyword(s):  
Information System ◽  
Data Processing ◽  
Data Base ◽  
Hospital Information System ◽  
Care Delivery ◽  
Traditional Approach ◽  
Processing System ◽  
Systems Design ◽  
Data Processing System ◽  
Karolinska Hospital

The paper gives a report on the Karolinska Hospital Information System in three parts.In part I, the information problems in health care delivery are discussed and the approach to systems design at the Karolinska Hospital is reported, contrasted, with the traditional approach.In part II, the data base and the data processing system, named T1—J 5, are described.In part III, the applications of the data base and the data processing system are illustrated by a broad description of the contents and rise of the patient data base at the Karolinska Hospital.

Data Screening Methods – Application to Differential Diagnosis in Pancreatic Pathology from Radiological Signs

1978 ◽  
Vol 17 (01) ◽  
pp. 36-40 ◽  
Author(s):  
J.-P. Durbec ◽  
Jaqueline Cornée ◽  
P. Berthezene
Keyword(s):  
Differential Diagnosis ◽  
Mutual Information ◽  
Data Processing ◽  
Screening Methods ◽  
Automatic Data ◽  
Chronic Calcifying Pancreatitis ◽  
Number Of Patients ◽  
Calcifying Pancreatitis ◽  
Pancreatic Pathology ◽  
Data Screening

The practice of systematic examinations in hospitals and the increasing development of automatic data processing permits the storing of a great deal of information about a large number of patients belonging to different diagnosis groups.To predict or to characterize these diagnosis groups some descriptors are particularly useful, others carry no information. Data screening based on the properties of mutual information and on the log cross products ratios in contingency tables is developed. The most useful descriptors are selected. For each one the characterized groups are specified.This approach has been performed on a set of binary (presence—absence) radiological variables. Four diagnoses groups are concerned: cancer of pancreas, chronic calcifying pancreatitis, non-calcifying pancreatitis and probable pancreatitis. Only twenty of the three hundred and forty initial radiological variables are selected. The presence of each corresponding sign is associated with one or more diagnosis groups.

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