User Pattern Learning Algorithm based MDSS(Medical Decision Support System) Framework under Ubiquitous
In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).
[1] H.S Lee M, S.J Beak, M.S Chol, C.S Park. : High risk patient death before
arriving to hospital J Korean Acad Fam Med Vol. 14, 1993: 601-08.
[2] Milos Hauskret, Hamish Fraser.: Planning Treatment of Ischemic Heart
Disease with Partially Observable Markov Decision Process. Artificial
Intelligence in Medicine, vol 18, 2000 221-44.
[3] Anton F.P. van Putten, Dave J. Hitchigs, Philip H. Quaqjer.: Portable
Electronic Peak Flowmeter for Improved Diagnosis of Chest Diseases in
COPD Patients. Instrumentation and Measurement Technology
Conference, 1993.
[4] D. A. Newandee, S. S. Reisman, M. N. Bartels, and R. E. De Meersman.:
COPD Severity Classification Using Principal Component and Cluster
Analysis on HRV Parameters. Bioengineering Conference, 2003 IEEE
29th Annual, Proceedings March 2003:134-35.
[5] Dr. K. Karoui, Dr. R. Sammouda, Dr. M. Sammouda.: Framework for a
Telemedicine Multilevel Diagnose System, Proceedings of the 23rd
annual EMBS International Conference, October 25-28, Istanbul, Turkey.
[6] Richard P. Lippmann,:" An introduction to computing with neural
network", IEEE ASSP magazine, 1987, pp. 4-22
[7] Devinder Thapa, Insung Jung, Chang Mok Park, and Gi-Nam Wang.:
Intelligent Agent Based Multi Perspective Dynamic Decision Model for
Ubiquitous Healthcare System, CIS05, accepted for Int-l workshop,
XIAN, China, December 15-16, 2005.
[8] Devinder Thapa, In-Sung Jung and Gi-Nam Wang: Agent Based Decision
Support System using Reinforcement Learning under Emergency
Circumstances, Spriger Lecture Notes in Computer Science (LNCS),
3610, pp 888-892, Changsa, China, August 27-29, 2005.
[1] H.S Lee M, S.J Beak, M.S Chol, C.S Park. : High risk patient death before
arriving to hospital J Korean Acad Fam Med Vol. 14, 1993: 601-08.
[2] Milos Hauskret, Hamish Fraser.: Planning Treatment of Ischemic Heart
Disease with Partially Observable Markov Decision Process. Artificial
Intelligence in Medicine, vol 18, 2000 221-44.
[3] Anton F.P. van Putten, Dave J. Hitchigs, Philip H. Quaqjer.: Portable
Electronic Peak Flowmeter for Improved Diagnosis of Chest Diseases in
COPD Patients. Instrumentation and Measurement Technology
Conference, 1993.
[4] D. A. Newandee, S. S. Reisman, M. N. Bartels, and R. E. De Meersman.:
COPD Severity Classification Using Principal Component and Cluster
Analysis on HRV Parameters. Bioengineering Conference, 2003 IEEE
29th Annual, Proceedings March 2003:134-35.
[5] Dr. K. Karoui, Dr. R. Sammouda, Dr. M. Sammouda.: Framework for a
Telemedicine Multilevel Diagnose System, Proceedings of the 23rd
annual EMBS International Conference, October 25-28, Istanbul, Turkey.
[6] Richard P. Lippmann,:" An introduction to computing with neural
network", IEEE ASSP magazine, 1987, pp. 4-22
[7] Devinder Thapa, Insung Jung, Chang Mok Park, and Gi-Nam Wang.:
Intelligent Agent Based Multi Perspective Dynamic Decision Model for
Ubiquitous Healthcare System, CIS05, accepted for Int-l workshop,
XIAN, China, December 15-16, 2005.
[8] Devinder Thapa, In-Sung Jung and Gi-Nam Wang: Agent Based Decision
Support System using Reinforcement Learning under Emergency
Circumstances, Spriger Lecture Notes in Computer Science (LNCS),
3610, pp 888-892, Changsa, China, August 27-29, 2005.
@article{"International Journal of Information, Control and Computer Sciences:56283", author = "Insung Jung and Gi-Nam Wang", title = "User Pattern Learning Algorithm based MDSS(Medical Decision Support System) Framework under Ubiquitous", abstract = "In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).", keywords = "Neural network, U-healthcare, MDSS, CAP, DSS.", volume = "1", number = "12", pages = "3888-5", }