Mining Clinical Data: Selecting Decision Support Algorithm for the MET-AP System Szymon Wilk Poznan University of Technology Ken Farion Children’s Hospital of Eastern Ontario Wojtek Michalowski University of Ottawa Jerzy Blaszczynski Poznan University of Technology Steven Rubin Children’s Hospital of Eastern Ontario Dawid Weiss Poznan University of Technology

Emergency Triage  Triage ≠ diagnosis Prioritization (Triage nurse) Medical assessment and disposition (Physician) Consult Observation/ further investigation Discharge Canadian Triage Acuity Scale (CTAS) What clinical algorithm should be used?

Experiment  Retrospective study of the ED patients with abdominal pain (AP)  Data transcribed from the selected records  Considered algorithms Rule-based Naive Bayes Case-based Tree-based # of records Triage classLearning dataTesting data Discharge35252 Observation8915 Consult16533 Total606100

Results AlgorithmOverallDischargeObservationConsult Rule-based59.00%55.80%46.70%69.70% Naive Bayes56.00%65.40%20.00%57.60% Case-based58.00%57.70%20.00%75.80% Tree-based57.00%59.60%20.00%69.70% AlgorithmOverallDischargeObservationConsult Naive Bayes56.00%59.60%46.70%54.60% Case-based49.00%42.30%60.00%54.60% Tree-based55.00%59.60%40.00%54.60% Classification accuracy Cost-sensitive classification accuracy AlgorithmSensitivitySpecificityGain Rule-based Naive Bayes Case-based Tree-based AlgorithmSensitivitySpecificityGain Naive Bayes Case-based Tree-based

MET-AP  Client-server architecture with the embedded rule- based clinical algorithm  Verified in a clinical trial Shell Local database MET Client AP clinical algorithm HIS Clinical alorithms Integrator Temporary database MET Server HL7 wired or wireless communication PhysiciansMET-AP Overall70.24%72.21% Discharge71.26%80.17% Observation63.93%29.51% Consult70.83%68.75% Accuracy in the trial

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