User Pattern Learning Algorithm based MDSS(Medical Decision Support System) Framework under Ubiquitous

Authors: Insung Jung, Gi-Nam Wang

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.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333374

References:

[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.