Artif Intell Med - A knowledge-based clinical toxicology consultant for diagnosing multiple exposures.

Tópicos

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Resumo

JECTIVE: 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.

Resumo Limpo

jectiv paper present continu research toward develop knowledgebas system diagnosi human toxic exposur particular research focus challeng task diagnos exposur multipl toxin although toxic exposur unit state involv multipl toxin multipl exposur account half toxinrel fatal use simpl medic mathemat seek produc practic decis support system capabl suppli use inform aid diagnosi complex case involv multipl unknown substancesmethod system automat train use data mine techniqu extract prior probabl likelihood ratio databas manag florida poison inform center fpic suppli observ clinic effect system produc rank list plausibl toxic exposur test system diagnos toxin three level identifi substanc identifi toxin major minor categori identifi toxin major categori alon enabl comparison three level accuraci calcul percentag exposur correct identifi top train diagnosesresult system evalu util dataset multipl exposur case singl exposur case initi system test use multipl exposur case yield poor result diagnosi accuraci rang investig reveal system inabl diagnos multipl disord result insuffici data clinic effect observ multipl exposur domin singl substanc includ singl exposur train system achiev accuraci high diagnos primari contributor multipl exposur case substanc diagnos major minor categori diagnos major categori aloneconclus although system fail complet diagnos exposur multipl toxin abil identifi primari contributor case may prove valuabl aid medic personnel seek diagnos treat patient time pass case ad fpic databas believ system accuraci will continu improv produc viabl decis support system clinic toxicolog

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