title: A data mining technique for risk-stratification diagnosis.
abstract: We describe a data mining model for sequential diagnosis, called the Optimal Decision Path Finder (ODPF), which is built based on the idea of risk stratification. A filter was used to stratify patients depending on ease of diagnosis, and a series of patient-specific classifiers was built to diagnose with confidence while reducing exam cost. Results show that applying stratification to data mining can improve the diagnostic performance and reduce waste of medical resource. This resulting model can assist the physician in triage decisions.
authors.affiliation: Health Informatics Program,University of Iowa. USA.
authors.forename: Chih-Lin
authors.initials: CL
authors.lastname: Chi
authors.forename: W Nick
authors.initials: WN
authors.lastname: Street
mesh_terms: D003936:Diagnosis, Computer-Assisted; D006331:Heart Diseases / Q000175:diagnosis; D006801:Humans; D016247:Information Storage and Retrieval; D020379:Risk Adjustment