A knowledge-based toxicology consultant for diagnosing multiple disorders
A. Antonio Arroyo, Douglas D. Dankel, Joel D. Schipper · 2008
Every year, toxic exposures kill twelve hundred Americans. More than half of these deaths are the result of exposures to multiple substances. In addition to being dangerous, multiple exposures are particularly difficult to diagnose. At this time, no general solution exists for the diagnosis of multiple disorders due to the non-linear interactions observed in such cases. This dissertation presents the development of a prototype knowledge-based system for diagnosing toxic exposures. The goal of the system is to generate differential diagnoses for unknown exposure cases based on the clinical effects observed in patients. The system is not meant to replace physicians, but, rather, to serve as a medical decision support system. Acting as a consultant, the system provides access to case-based summary data that is normally unavailable. The system is automatically generated by applying data mining techniques to a database supplied by the Florida Poison Information Center. For diagnosis, the system uses pre-test probabilities and likelihood ratios—calculations commonly used throughout the medical profession. To overcome certain shortcomings of likelihood ratios, the equation employed by the system is adjusted to account for every possible outcome. Using the adjusted likelihood ratio enables robust calculations while closely modeling the likelihood ratio that physicians know and trust. Trained and tested on single exposures, the system achieved an accuracy of 81.0% on cases involving at least three clinical effects. Repeating the process for multiple exposures alone resulted in a failure, at least partially due to insufficient data. However, training on various combinations of single, double, and/or multiple exposures, the system achieved an accuracy of 86.9% when diagnosing the primary contributors for multiple exposure cases. Although a solution for diagnosing multiple disorders remains elusive, the ability to identify primary contributors is a significant contribution to addressing the problem. This system is the first American diagnostic system for the field of clinical toxicology and its use of adjusted likelihood ratios serves as a method to bridge the gap between intelligent systems and the medical field. Furthermore, by automatically generating the system, this research addresses the knowledge acquisition bottleneck that plagues traditional expert systems.