Electrical and Computer Engineering, Embry-Riddle Aeronautical University, 3700 Willow Creek Road, Prescott, AZ 86301, USA.
Artif Intell Med. 2012 Jun;55(2):87-95. doi: 10.1016/j.artmed.2012.03.006. Epub 2012 Apr 21.
Every year, toxic exposures kill 1200 Americans. To aid in the timely diagnosis and treatment of such exposures, this research investigates the feasibility of a knowledge-based system capable of generating differential diagnoses for human exposures involving unknown toxins.
Data mining techniques automatically extract prior probabilities and likelihood ratios from a database managed by the Florida Poison Information Center. Using observed clinical effects, the trained system produces a ranked list of plausible toxic exposures. The resulting system was evaluated using 30,152 single exposure cases. In addition, the effects of two filters for refining diagnosis based on a minimum number of exposure cases and a minimum number of clinical effects were also explored.
The system achieved accuracies (calculated as the percentage of exposures correctly identified in top 10% of trained diagnoses) as high as 79.8% when diagnosing by substance and 78.9% when diagnosing by the major and minor categories of toxins.
The results of this research are modest, yet promising. At this time, no similar systems are currently in use in the United States and it is hoped that these studies will yield an effective medical decision support system for clinical toxicology.
每年，有毒物质暴露都会导致 1200 名美国人死亡。为了帮助及时诊断和治疗此类暴露，本研究调查了一种基于知识的系统的可行性，该系统能够针对涉及未知毒素的人类暴露生成鉴别诊断。
数据挖掘技术自动从佛罗里达毒物信息中心管理的数据库中提取先验概率和似然比。使用观察到的临床效果，经过训练的系统生成了可能的有毒暴露的排名列表。使用 30152 个单一暴露病例评估了所得系统。此外，还探讨了基于最小暴露病例数和最小临床效果数对诊断进行细化的两个过滤器的效果。
该系统在按物质诊断时的准确率（计算为前 10%的训练诊断中正确识别的暴露百分比）高达 79.8%，在按毒素的主要和次要类别诊断时的准确率为 78.9%。
这些研究结果虽然不大，但很有希望。目前，美国没有类似的系统在使用，希望这些研究能够为临床毒理学提供有效的医疗决策支持系统。
