An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents

TL;DR

This study evaluates the effectiveness of WISER, BDT, and ANN in rapidly and accurately identifying chemical agents during mass casualty incidents, highlighting the potential of neural networks and dimensional reduction techniques.

Contribution

The paper compares WISER, BDT, and ANN methods for chemical identification, introducing a dimensional reduction approach with minimal performance loss.

Findings

WISER achieved 1.8% accuracy range in tests.

ANN outperformed BDT in accuracy.

Dimensional reduction from 79 to 40 variables maintained performance.

Abstract

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.