Summary

Despite the unfamiliarity of most readers with diagnostic expert systems, we chose to begin our discussion of algorithms for biosurveillance with this topic because case detection provides the case data needed for outbreak detection. Outbreak detection cannot function without case detection (unless the surveillance data are aggregated data, such as daily sales of over-the-counter thermometers).

Diagnostic expert systems have the potential to improve the quality and completeness of the case data available to analytic methods designed to detect and characterize outbreaks, which we discuss in the following chapters. They can profoundly improve the reporting of syndromes. Improvements in case detection will translate directly into improvements in the ear-liness of outbreak detection and characterization.

McDonald did not conclude in his seminal paper that the solution to the non-perfectibility of man was to admit only women to medical schools. Rather, he stated, "Thus, I conclude that though the individual physician is not perfectible, the system of care is, and that the computer will play a major part in the perfection of future care systems." His point—that technology can be used to create a system (involving both humans and computers) that can then be perfected—also seems to apply to biosurveillance systems.

12. ADDITIONAL RESOURCES_

12.1. Expert Systems in Plant Pathology

For readers who are interested in diagnostic expert systems for diagnosing plant diseases, Travis and Latin (1991) briefly reviewed several diagnostic expert systems in plant pathology, including PLANT/ed, Apple Pest and Disease Diagnosis, CALEX/Peaches, Muskmelon disorder management system, and Penn State Apple Orchard Consultant, in "Development, Implementation, and Adoption of Expert Systems in Plant Pathology."

13. ACKNOWLEDGMENTS_

This work was supported in part by grants from the National Science Foundation (IIS-0325581), the Defense Advanced Research Projects Agency (F30602-01-2-0550), and the Pennsylvania Department of Health (ME-01-737). We thank William Hogan and Gregory Cooper for helpful discussions and comments.

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