Mining clinical data: selecting decision support algorithm for the MET-AP system

Authors:
Jerzy Blaszczynski;Ken Farion;Wojtek Michalowski;Szymon Wilk;Steven Rubin;Dawid Weiss
Affiliations:
Institute of Computing Science, Poznan University of Technology, Poznan, Poland;Children's Hospital of Eastern Ontario, Ottawa, ON, Canada;School of Management, University of Ottawa, Ottawa, ON, Canada;Institute of Computing Science, Poznan University of Technology, Poznan, Poland;Children's Hospital of Eastern Ontario, Ottawa, ON, Canada;Institute of Computing Science, Poznan University of Technology, Poznan, Poland
Venue:
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Year:
2005

Citing 5
Cited 1

Instance-Based Learning Algorithms

Machine Learning
C4.5: programs for machine learning

C4.5: programs for machine learning
Coronary Heart Disease Patient Models Based on Inductive Machine Learning

AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice

EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Estimating continuous distributions in Bayesian classifiers

UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence

Survey paper: Knowledge discovery in clinical decision support systems for pain management: A systematic review

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

We have developed an algorithm for triaging acute pediatric abdominal pain in the Emergency Department using the discovery-driven approach. This algorithm is embedded into the MET-AP (Mobile Emergency Triage – Abdominal Pain) system – a clinical decision support system that assists physicians in making emergency triage decisions. In this paper we describe experimental evaluation of several data mining methods (inductive learning, case-based reasoning and Bayesian reasoning) and results leading to the selection of the rule-based algorithm.