A knowledge-based clinical toxicology consultant for diagnosing multiple exposures

• Published in: Artificial intelligence in medicine Vol. 58; no. 1; pp. 15 - 21
• Main Authors: Schipper, Joel D, Dankel, Douglas D, Arroyo, A. Antonio, Schauben, Jay L
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01-05-2013; Elsevier
• Subjects: Applied sciences; Biological and medical sciences; Clinical toxicology; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Consultants; Data mining; Data Mining - methods; Data processing. List processing. Character string processing; Decision support systems; Decision Support Systems, Clinical - organization & administration; Diagnosis, Differential; Differential diagnosis; Drug-Related Side Effects and Adverse Reactions - diagnosis; Drug-Related Side Effects and Adverse Reactions - etiology; Exact sciences and technology; Humans; Information systems. Data bases; Internal Medicine; Knowledge Bases; Knowledge-based systems; Medical management aid. Diagnosis aid; Medical sciences; Memory organisation. Data processing; Multiple disorder diagnosis; Other; Software; Toxicology; United States; North America; America; Decision support systems; Clinical toxicology; Differential diagnosis; Knowledge-based systems; Multiple disorder diagnosis; Data mining; Measurement; Information extraction; Biomedical data processing; Toxicology; Medical prescription; Database; Secondary effect; Diagnosis; Multilevel system; Medical application; Data analysis; Useful information; Decision support system; Hierarchical classification; Likelihood ratio; Data processing; Knowledge base; Prior probability; Educational software program; Consultant
• ISSN: 0933-3657; 1873-2860
• DOI: 10.1016/j.artmed.2013.02.002

Abstract 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.