Bayesian Classification of Respiratory Disease and Asthma with Administrative Data Sets

Probabilistic models of unsupervised classification are being applied with hospital discharge abstracts for respiratory disease, to form new classifications of asthma. Hierarchal classifications have been formed with AutoClass1, a public domain bayesian discovery system. AutoClass findings are being tested for biologic plausibility and compared with standard definitions of asthma provided by the Council of State and Territorial Epidemiologists2, and other national organizations. Approximately 60,000 records with 21 features were formed with hospital discharge abstracts, accounting for diagnoses, patient demographics, treatments and cost. Classes incorporating ecologic variables associated with patient county characteristics are also being tested