scholarly journals Temporal and spatial data mining with second-order hidden markov models

2005 ◽  
Vol 10 (5) ◽  
pp. 406-414 ◽  
Author(s):  
J.-F. Mari ◽  
F. Le Ber
Keyword(s):  
Data Mining ◽  
Hidden Markov Models ◽  
Spatial Data ◽  
Markov Models ◽  
Hidden Markov ◽  
Spatial Data Mining ◽  
Second Order ◽  
Temporal And Spatial

scholarly journals Second-Order Belief Hidden Markov Models

2014 ◽  
pp. 284-293 ◽  
Author(s):  
Jungyeul Park ◽  
Mouna Chebbah ◽  
Siwar Jendoubi ◽  
Arnaud Martin
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Second Order

Better Knowledge for Better Health Services

2011 ◽  
pp. 233-255
Author(s):  
Stefano De Luca ◽  
Enrico Memo
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Clinical Practice ◽  
Disease Management ◽  
Hidden Markov Models ◽  
Practice Guidelines ◽  
Markov Models ◽  
Hidden Markov ◽  
The Right ◽  
The Given

The expenses in Health Care are an important portion of the overall expenses of every country, so it is very important to determine if the given cares are the right ones. This work is about a methodology, Health Discoverer, and a consequent software, aimed to disease management and to the measure of appropriateness of cares, and in particular is about the data mining techniques used to verify Clinical Practice Guidelines (CPGs) compliance and the discovery of new, better guidelines. The work is based on Quality Records, episode parsing using Ontologies and Hidden Markov Models.

Better Knowledge for Better Health Services

2011 ◽  
pp. 65-87
Author(s):  
Stefano De Luca ◽  
Enrico Memo
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Clinical Practice ◽  
Disease Management ◽  
Hidden Markov Models ◽  
Practice Guidelines ◽  
Markov Models ◽  
Hidden Markov ◽  
The Right ◽  
The Given

The expenses in Health Care are an important portion of the overall expenses of every country, so it is very important to determine if the given cares are the right ones. This work is about a methodology, Health Discoverer, and a consequent software, aimed to disease management and to the measure of appropriateness of cares, and in particular is about the data mining techniques used to verify Clinical Practice Guidelines (CPGs) compliance and the discovery of new, better guidelines. The work is based on Quality Records, episode parsing using Ontologies and Hidden Markov Models.

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