Computer-aided diagnosis with potential application to rapid detection of disease outbreaks.

Our objectives are to quickly interpret symptoms of emergency patients to identify likely syndromes and to improve population-wide disease outbreak detection. We constructed a database of 248 syndromes, each syndrome having an estimated probability of producing any of 85 symptoms, with some two-way, three-way, and five-way probabilities reflecting correlations among symptoms. Using these multi-way probabilities in conjunction with an iterative proportional fitting algorithm allows estimation of full conditional probabilities.

Results from the fielding of the Bio-surveillance Analysis, Feedback, Evaluation and Response (B-SAFER) system in Albuquerque, New Mexico.

Public health authorities need a surveillance system that is sensitive enough to detect a disease outbreak early to enable a proper response. In order to meet this challenge we have deployed a pilot component-based system in Albuquerque, NM as part of the National Biodefense Initiative (BDI). B-SAFER gathers routinely collected data from healthcare institutions to monitor disease events in the community.