当前位置： SCI文献检索 > ARTIFICIAL INTELLIGENCE IN MEDICINE期刊下所有文献 > A knowledge-based clinical toxicology consultant for diagnosing multiple exposures.

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

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.

journal_name

Artif Intell Med

journal_title

Artificial intelligence in medicine

authors

Schipper JD,Dankel DD 2nd,Arroyo AA,Schauben JL

doi

10.1016/j.artmed.2013.02.002

subject

Has Abstract

pub_date

2013-05-01 00:00:00

pages

15-21

issue

eissn

0933-3657

issn

1873-2860

pii

S0933-3657(13)00010-9

journal_volume

pub_type

杂志文章

在线工具

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