TY - GEN
T1 - Collaborative intelligence for intelligent diagnosis systems in hospital environment
AU - Ha, Sung Ho
AU - Zhang, Zhenyu
PY - 2010
Y1 - 2010
N2 - Emergency Departments (ED) in a hospital is a complex unit where the fight between life and death is always in a breathing time. The ED has been frustrated by the problem of overcrowding and long time waiting for over decades. With the development of computer technology, various kinds of information systems have appeared and make people work more effectively. Emergency Department Information Systems (EDIS) have been heralded as a "must" for the modern ED and using of the EDIS can enhance patients care, decrease the waiting time, and reduce the situation of overcrowding. Data mining techniques has been using in medical researches for many years and has been found to be quite effective. The objective of this paper is to design the collaborative intelligence for interactive diagnosis systems as part of EDIS. Based on the patients flow in the ED, we utilize data mining techniques to generate predictive models to help physicians make diagnosis faster and more accurately. By using this decision-supporting system, physicians can work more effectively and the waiting times in ED can decrease.
AB - Emergency Departments (ED) in a hospital is a complex unit where the fight between life and death is always in a breathing time. The ED has been frustrated by the problem of overcrowding and long time waiting for over decades. With the development of computer technology, various kinds of information systems have appeared and make people work more effectively. Emergency Department Information Systems (EDIS) have been heralded as a "must" for the modern ED and using of the EDIS can enhance patients care, decrease the waiting time, and reduce the situation of overcrowding. Data mining techniques has been using in medical researches for many years and has been found to be quite effective. The objective of this paper is to design the collaborative intelligence for interactive diagnosis systems as part of EDIS. Based on the patients flow in the ED, we utilize data mining techniques to generate predictive models to help physicians make diagnosis faster and more accurately. By using this decision-supporting system, physicians can work more effectively and the waiting times in ED can decrease.
KW - Collaborative intelligence
KW - Component
KW - Data mining
KW - Emergency department
KW - Intelligent systems
KW - Medical diagnosis
UR - http://www.scopus.com/inward/record.url?scp=77952126884&partnerID=8YFLogxK
U2 - 10.1109/WKDD.2010.128
DO - 10.1109/WKDD.2010.128
M3 - Conference contribution
AN - SCOPUS:77952126884
SN - 9780769539232
T3 - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
SP - 532
EP - 535
BT - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
T2 - 3rd International Conference on Knowledge Discovery and Data Mining, WKDD 2010
Y2 - 9 January 2010 through 10 January 2010
ER -