@article{(Open Science Index):https://publications.waset.org/pdf/10229,
	  title     = {Data Mining Applied to the Predictive Model of Triage System in Emergency Department},
	  author    = {Wen-Tsann Lin and  Yung-Tsan Jou and  Yih-Chuan Wu and  Yuan-Du Hsiao},
	  country	= {},
	  institution	= {},
	  abstract     = {The Emergency Department of a medical center in
Taiwan cooperated to conduct the research. A predictive model of
triage system is contracted from the contract procedure, selection of
parameters to sample screening. 2,000 pieces of data needed for the
patients is chosen randomly by the computer. After three
categorizations of data mining (Multi-group Discriminant Analysis,
Multinomial Logistic Regression, Back-propagation Neural
Networks), it is found that Back-propagation Neural Networks can
best distinguish the patients- extent of emergency, and the accuracy
rate can reach to as high as 95.1%. The Back-propagation Neural
Networks that has the highest accuracy rate is simulated into the triage
acuity expert system in this research. Data mining applied to the
predictive model of the triage acuity expert system can be updated
regularly for both the improvement of the system and for education
training, and will not be affected by subjective factors.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {7},
	  number    = {6},
	  year      = {2013},
	  pages     = {834 - 841},
	  ee        = {https://publications.waset.org/pdf/10229},
	  url   	= {https://publications.waset.org/vol/78},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 78, 2013},