Generation of an intelligent medical system, using a real database, to diagnose bacterial infection in hospitalized...

The initial diagnosis of bacterial infections in the absence of laboratory microbiological data requires physicians to use clinical algorithms based on symptoms, patient history and infection site. Optimization of such algorithms would be achieved by including as many variables associated with bacterial infection as possible. Demographic data are easily available and frequently used to sub-group human populations. A prospective investigation was, therefore, undertaken to examine the influence of demographic variables on bacterial infection rates, using data obtained from 173 patients presenting to Albert Einstein Medical Center. Data was randomly selected from 149 of these patients and used to generate fuzzy rules to model an intelligent medical system. To test the accuracy of this system at determining bacterial infection, based solely on demographic data, the program was given the remaining 24 patients' information. All 18 patients with either streptococcal, staphylococcal or Escherichia coli infections were correctly diagnosed. Non-E.coli GNR were misdiagnosed as E. coli infections in two patients resulting in an overall prediction rate for the 24 patients of 91.66%. This study suggests that the direct correlation of demographic variables with a predisposition to bacterial infection allow the design of an intelligent medical system, which shows great future potential as a diagnostic tool for all physicians.