IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
A comfort measure for diagnostic problem solving
Information Sciences: an International Journal
Medical decision making: probabilistic medical reasoning
Medical informatics: computer applications in health care
Inductive Learning for Case-Based Diagnosis with Multiple Faults
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
ADAPtER: An Integrated Diagnostic System Combining Case-Based and Abductive Reasoning
ICCBR '95 Proceedings of the First International Conference on Case-Based Reasoning Research and Development
Masquerade Detection Using Truncated Command Lines
DSN '02 Proceedings of the 2002 International Conference on Dependable Systems and Networks
Extension of the HEPAR II Model to Multiple-Disorder Diagnosis
Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
Functional Evaluation of SETH: An Expert System In Clinical Toxicology
AIME '95 Proceedings of the 5th Conference on Artificial Intelligence in Medicine in Europe: Artificial Intelligence Medicine
Principles of human-computer collaboration for knowledge discovery in science
Artificial Intelligence
Efficient diagnosis of multiple disorders based on a symptom clustering approach
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Pattern-Based Interactive Diagnosis of Multiple Disorders: The MEDAS System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluating Multimembership Classifiers: A Methodology and Application to the MEDAS Diagnostic System
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality measures and semi-automatic mining of diagnostic rule bases
INAP'04/WLP'04 Proceedings of the 15th international conference on Applications of Declarative Programming and Knowledge Management, and 18th international conference on Workshop on Logic Programming
A knowledge-based clinical toxicology consultant for diagnosing single exposures
Artificial Intelligence in Medicine
Data mining a diabetic data warehouse
Artificial Intelligence in Medicine
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Objective: This paper presents continued research toward the development of a knowledge-based system for the diagnosis of human toxic exposures. In particular, this research focuses on the challenging task of diagnosing exposures to multiple toxins. Although only 10% of toxic exposures in the United States involve multiple toxins, multiple exposures account for more than half of all toxin-related fatalities. Using simple medical mathematics, we seek to produce a practical decision support system capable of supplying useful information to aid in the diagnosis of complex cases involving multiple unknown substances. Methods: The system is automatically trained using data mining techniques to extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center (FPIC). When supplied with observed clinical effects, the system produces a ranked list of the most plausible toxic exposures. During testing, the system diagnosed toxins at three levels: identifying the substance, identifying the toxin's major and minor categories, and identifying the toxin's major category alone. To enable comparison between these three levels, accuracy was calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses. Results: System evaluation utilized a dataset of 8901 multiple exposure cases and 37,617 single exposure cases. Initial system testing using only multiple exposure cases yielded poor results, with diagnosis accuracies ranging from 18.5% to 50.1%. Further investigation revealed that the system's inability to diagnose multiple disorders resulted from insufficient data and that the clinical effects observed in multiple exposures are dominated by a single substance. Including single exposures when training, the system achieved accuracies as high as 83.5% when diagnosing the primary contributors in multiple exposure cases by substance, 86.9% when diagnosing by major and minor categories, and 79.9% when diagnosing by major category alone. Conclusions: Although the system failed to completely diagnose exposures to multiple toxins, the ability to identify the primary contributor in such cases may prove valuable in aiding medical personnel as they seek to diagnose and treat patients. As time passes and more cases are added to the FPIC database, we believe system accuracy will continue to improve, producing a viable decision support system for clinical toxicology.