Data Mining Applied to the Predictive Model of Triage System in Emergency Department
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.
[1] X. K. Zhou, "Discussion on the features of high emergency resources
consumers by data mining technology (unpublished thesis) ", National
Taiwan University, Taiwan, 2004.
[2] Department of Health, DOH, Appraisal Standard of Emergency
Departments. Website of DOH, Executive Yuan, Taiwan, 2009.
[3] A. Wollaston, P. Fahey, M. McKay, D. Hegney, P. Miller, & J.
Wollaston, "Reliability and validity of the toowoomba adult trauma triage
tool: a Queensland, Australia study," Accident and Emergency Nursing,
vol.12(4), 2004, pp.230-237.
[4] R. M. Russo, V. J. Gururaj, A. S. Bunye, Y. H. Kim, & S. Ner, "Triage
abilities of nurse practitioner vs. pediatrician," American Journal of
Disease of Children, 129(6), 1975, pp.673-675.
[5] J. Y. Zhan, "Discussion on the accuracy rate of the nursing staff-s
emergency triage and its correlation between the decision making
capability (unpublished thesis) ", National Taipei College of Nursing,
Taiwan, 2003.
[6] J. Brillman, D. Doezema, & D. Tandberg, "Triage´╝ÜLimitations in
predicting the need for emergency care and hospital admissions," Annals
of Emergency Medicine, vol.27(4), 1996, pp.493-500.
[7] E. G. Estrada, "Triage Systems," Nursing Clinics of North America,
vol.16(1), 1981, pp.13-22.
[8] A. VanBoxel, "How We Do It: Improving the triage process," Journal of
Emergency Nursing, vol.21(4), 1995, pp.332-334.
[9] J. Reinschmidt, H. Gottschalk, H. Kim, & D. Zwietering, Intelligent
Miner for Data: Enhance Your Business Intelligence. USA: IBM
International Technical Support Organization. 1999.
[10] M. L. Huang, & H. Y. Chen, "Development and comparison of automated
classifiers for glaucoma diagnosis using stratus optical coherence
tomography," Investigative Ophthalmology & Visual Science, vol.46(11),
2005, pp.4121-4129.
[11] M. A. Abdelfattah, A. T. El-Shahat, M. E. Ahmad, A. A. Mosaad, M. O.
Mohamed, & E. S. Gamal, "Discrimination function based on hyaluronic
acid and its degrading enzymes and degradation products for
differentiating crrhotic from non-cirrhotic liver diseased patients in
chronic HCV infection," Clinica Chimica Acta, vol.369(1), 2006,
pp.66-72.
[12] E. Turban, J. E. Aronson, & T. P. Liang, Decision Support and Intelligent
Systems (7th ed.). Pearson: Prentice Hall, 2005.
[13] R. Sharda, & D. Delen, "Predicting box-office success of motion pictures
with neural networks," Expert Systems with Applications, vol.30(2), 2006,
pp.243-254.
[14] A. M. Heidar, B. K. Nicolaos, & B. Mahesh, "Short-tern electric power
load forecasting using feedforward neural networks," Expert Systems,
vol.21(3), 2004, pp. 157-166.
[15] G. Handyside, Triage in Emergency Practice. St. Louis, MO: Mosby,
1996.
[16] Y. C. Ye, The Application and Practice of Neural Network Models,
Taipei: Scholar Books, 2011
[1] X. K. Zhou, "Discussion on the features of high emergency resources
consumers by data mining technology (unpublished thesis) ", National
Taiwan University, Taiwan, 2004.
[2] Department of Health, DOH, Appraisal Standard of Emergency
Departments. Website of DOH, Executive Yuan, Taiwan, 2009.
[3] A. Wollaston, P. Fahey, M. McKay, D. Hegney, P. Miller, & J.
Wollaston, "Reliability and validity of the toowoomba adult trauma triage
tool: a Queensland, Australia study," Accident and Emergency Nursing,
vol.12(4), 2004, pp.230-237.
[4] R. M. Russo, V. J. Gururaj, A. S. Bunye, Y. H. Kim, & S. Ner, "Triage
abilities of nurse practitioner vs. pediatrician," American Journal of
Disease of Children, 129(6), 1975, pp.673-675.
[5] J. Y. Zhan, "Discussion on the accuracy rate of the nursing staff-s
emergency triage and its correlation between the decision making
capability (unpublished thesis) ", National Taipei College of Nursing,
Taiwan, 2003.
[6] J. Brillman, D. Doezema, & D. Tandberg, "Triage´╝ÜLimitations in
predicting the need for emergency care and hospital admissions," Annals
of Emergency Medicine, vol.27(4), 1996, pp.493-500.
[7] E. G. Estrada, "Triage Systems," Nursing Clinics of North America,
vol.16(1), 1981, pp.13-22.
[8] A. VanBoxel, "How We Do It: Improving the triage process," Journal of
Emergency Nursing, vol.21(4), 1995, pp.332-334.
[9] J. Reinschmidt, H. Gottschalk, H. Kim, & D. Zwietering, Intelligent
Miner for Data: Enhance Your Business Intelligence. USA: IBM
International Technical Support Organization. 1999.
[10] M. L. Huang, & H. Y. Chen, "Development and comparison of automated
classifiers for glaucoma diagnosis using stratus optical coherence
tomography," Investigative Ophthalmology & Visual Science, vol.46(11),
2005, pp.4121-4129.
[11] M. A. Abdelfattah, A. T. El-Shahat, M. E. Ahmad, A. A. Mosaad, M. O.
Mohamed, & E. S. Gamal, "Discrimination function based on hyaluronic
acid and its degrading enzymes and degradation products for
differentiating crrhotic from non-cirrhotic liver diseased patients in
chronic HCV infection," Clinica Chimica Acta, vol.369(1), 2006,
pp.66-72.
[12] E. Turban, J. E. Aronson, & T. P. Liang, Decision Support and Intelligent
Systems (7th ed.). Pearson: Prentice Hall, 2005.
[13] R. Sharda, & D. Delen, "Predicting box-office success of motion pictures
with neural networks," Expert Systems with Applications, vol.30(2), 2006,
pp.243-254.
[14] A. M. Heidar, B. K. Nicolaos, & B. Mahesh, "Short-tern electric power
load forecasting using feedforward neural networks," Expert Systems,
vol.21(3), 2004, pp. 157-166.
[15] G. Handyside, Triage in Emergency Practice. St. Louis, MO: Mosby,
1996.
[16] Y. C. Ye, The Application and Practice of Neural Network Models,
Taipei: Scholar Books, 2011
@article{"International Journal of Information, Control and Computer Sciences:59288", author = "Wen-Tsann Lin and Yung-Tsan Jou and Yih-Chuan Wu and Yuan-Du Hsiao", title = "Data Mining Applied to the Predictive Model of Triage System in Emergency Department", 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.", keywords = "Back-propagation Neural Networks, Data Mining, Emergency Department, Triage System.", volume = "7", number = "6", pages = "800-8", }
