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Abnormal Diagnosis Of Emergency Department Triage Explored With



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  • 1. Abnormal diagnosis of Emergency Department triage explored with data mining technology : An Emergency Department at a Medical Center in Taiwan taken as an example
    • Wen -Tsann Lin ,Shen – Tsu Wang ,Ta-Cheng ,Yu-Xin Shi , Wei-yu Chen , Huei-min Chen
    • Present by : Umagorn Khaosumain ;KM 6
  • 2. TRIAGE Triage is used in medical emergencies, as a system to sort the injured so that the greatest number of patients can be helped. In medical emergency situations where there are more injured people than there are medical resources to care for them, triage allows doctors or other health care professionals to decide which people can be helped the most, and how to help them efficiently.
  • 3. Emergency Department assessment Standard(1995) 4 Level
    • Level 1 : Requiring immediate treatment
    • Level 2 : To be treated in 10 minutes
    • Level 3 : To be treated in 30 minutes
    • Level 4 : Treatment can be delayed
  • 4.
    • The study is made on medical management and nursing, with the knowledge of the administrative head at the Emergency Department ,in the hope to effectively improve consistency of triage with the combination of data mining theories and practice
  • 5. The purposes are as follows :
    • Base on information management ,the information system is applied in triage of the Emergency Department to generate patients’ data
    • Exploration of correlation between triage and abnormal diagnosis ; cluster analysis conducted on variables with clinical meanings
    • Establishing triage abnormal diagnosis clusters with hierarchical clustering (Ward ’method) and partitioning clustering (K-means algorithm); obtaining correlation law of abnormal diagnosis with decision trees
    • Improving consistency of triage with data mining ;offering quantified and scientific rules for triage decision -making in the hope of serving as a foundation for future researchers and clinical examination
  • 6. Fig.1.Number of service person times in past years. source:Department of Health,2004.
  • 7. Fig.2.Major component factors of triage
  • 8. Parameter measurement
    • Temperature
    • Systolic pressure
    • Diastolic pressure
    • Respiration
    • Pulse
    • SpO2
  • 9. Fig.3.Data mining process
  • 10. Data mining Process Model
    • Include:
    • Classification
    • Estimation
    • Prediction
    • Affinity grouping
    • Clustering
    • Description
    • Profiling
  • 11. Fig.4.Tendency after clustering
  • 12. Fig.5.Triage expert system (IDEFO-AO)