论文标题
用于快速准确识别质量伤亡事件中化学药物的AI模型
An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents
论文作者
论文摘要
在本报告中，我们研究了在基于化学的质量伤亡事件（MCI）期间，明智的罪魁祸首在化学罪犯识别的有效性。我们还使用与明智的实验条件来评估和比较二进制决策树（BDT）和人工神经网络（ANN）。使用训练组和31,00个模拟的患者记录用作测试集，将逆向工程的符号/症状组合起来。每种化学记录的体征/症状扰动产生了三组模拟的患者记录。尽管所有三种方法都达到了100％的训练准确性，但Wiser，BDT和ANN的表现分别为：1.8％-0％，65％-26％，67％-21％。使用ANN对尺寸降低的初步研究表明，尺寸从79个变量倒塌到40，而分类性能损失很少。
In this report we examine the effectiveness of WISER in identification of a chemical culprit during a chemical based Mass Casualty Incident (MCI). We also evaluate and compare Binary Decision Tree (BDT) and Artificial Neural Networks (ANN) using the same experimental conditions as WISER. The reverse engineered set of Signs/Symptoms from the WISER application was used as the training set and 31,100 simulated patient records were used as the testing set. Three sets of simulated patient records were generated by 5%, 10% and 15% perturbation of the Signs/Symptoms of each chemical record. While all three methods achieved a 100% training accuracy, WISER, BDT and ANN produced performances in the range of: 1.8%-0%, 65%-26%, 67%-21% respectively. A preliminary investigation of dimensional reduction using ANN illustrated a dimensional collapse from 79 variables to 40 with little loss of classification performance.
