Use of sequential Bayesian model in diagnosis of jaundice by computer.
A sequential Bayesian model has been developed for a computer and used to diagnose jaundiced patients admitted to hospital. Up to 102 items of information from the history, physical examination, and special investigations available within 48 hours of admission were collected on 309 patients. The results from these patients were used to calculate the probabilities of 11 possible diseases in 65 new patients and also to place patients into groups for medical or surgical treatment.The overall accuracy of the model in diagnosing patients as having one of 11 diseases was 69%, and where the final probability reached > 0.96, it was 89%. The overall accuracy in making a medical or surgical decision was 89%, and where the final probability reached > 0.96 it was 94%.Improvement in accuracy should result as the number of cases seen with rare conditions increases, and probably a similar model could be developed and used to make most use of those indicants with the highest cost-effectiveness.
D000208:Acute Disease; D002908:Chronic Disease; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006506:Hepatitis A / Q000175:diagnosis; D006801:Humans; D006933:Hyperbilirubinemia, Hereditary / Q000175:diagnosis; D007565:Jaundice / Q000139:chemically induced / Q000175:diagnosis*; D008103:Liver Cirrhosis / Q000175:diagnosis; D008105:Liver Cirrhosis, Biliary / Q000175:diagnosis; D008107:Liver Diseases / Q000175:diagnosis; D008113:Liver Neoplasms / Q000175:diagnosis; D008487:Medical History Taking; D008954:Models, Biological; D010808:Physical Examination; D011336:Probability; D013997:Time Factors