The optimal diagnostic decision sequence.
We describe a data mining model for constructing an optimal diagnostic sequence that assists cost-effective sequential decisions. We use heuristic search, i.e., hill climbing and genetic algorithms (GAs), and the evaluation function of cost-based Mean Accuracy Gain (cMAG), which is provided by SVM classifiers, to find this optimal sequence. GA can find a good sequence because of the ability to escape from local optima.
Chi
Chih-Lin
CL
Health Informatics Program, University of Iowa, USA.
Street
W Nick
WN
D000465:Algorithms; D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical / Q000458:organization & administration*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D016347:Medical Records Systems, Computerized / Q000458:organization & administration*; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods*; D011785:Quality Assurance, Health Care / Q000379:methods*; D014481:United States