An AI model for Rapid and Accurate Identification of Chemical Agents in Mass Casualty Incidents
This addresses rapid chemical identification for emergency responders in mass casualty incidents, but it is incremental as it compares existing methods on new simulated data.
The study evaluated three AI models (WISER, Binary Decision Tree, and Artificial Neural Networks) for identifying chemical agents in mass casualty incidents, finding that WISER achieved near-perfect performance (1.8%-0% error) compared to 65%-26% and 67%-21% for the others on simulated patient data.
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.