一种用于在大规模伤亡事件中快速准确识别化学制剂的人工智能模型。
An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents.
作者信息
Boltin Nicholas, Vu Daniel, Janos Bethany, Shofner Alyssa, Culley Joan, Valafar Homayoun
机构信息
Department of Computer Science and Engineering, University of South Carolina, Columbia, SC.
College of Nursing, University of South Carolina, Columbia, SC.
出版信息
HIMS 2016 (2016). 2016 Jul;2016:169-175.
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.
在本报告中，我们研究了WISER在基于化学品的大规模伤亡事件（MCI）中识别化学罪魁祸首的有效性。我们还在与WISER相同的实验条件下评估和比较了二叉决策树（BDT）和人工神经网络（ANN）。从WISER应用程序反向工程的体征/症状集用作训练集，31,100条模拟患者记录用作测试集。通过对每个化学记录的体征/症状进行5%、10%和15%的扰动生成了三组模拟患者记录。虽然所有三种方法都达到了100%的训练准确率，但WISER、BDT和ANN的表现分别在1.8%-0%、65%-26%、67%-21%的范围内。使用ANN进行降维的初步研究表明，维度从79个变量坍缩到40个，分类性能几乎没有损失。
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