Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents

TL;DR

This study explores how statistical dimension reduction techniques can streamline data for neural network models, enhancing emergency triage efficiency during chemical mass casualty incidents by reducing required signs and symptoms without sacrificing accuracy.

Contribution

It introduces the application of four dimension reduction methods to improve neural network decision-making in chemical MCIs, demonstrating effective data reduction and performance enhancement.

Findings

Signs and symptoms can be reduced to nearly 40 without losing accuracy.

Dimension reduction improves neural network performance.

Reduced data maintains high model accuracy.

Abstract

Chemical Mass Casualty Incidents (MCI) place a heavy burden on hospital staff and resources. Machine Learning (ML) tools can provide efficient decision support to caregivers. However, ML models require large volumes of data for the most accurate results, which is typically not feasible in the chaotic nature of a chemical MCI. This study examines the application of four statistical dimension reduction techniques: Random Selection, Covariance/Variance, Pearson's Linear Correlation, and Principle Component Analysis to reduce a dataset of 311 hazardous chemicals and 79 related signs and symptoms (SSx). An Artificial Neural Network pipeline was developed to create comparative models. Results show that the number of signs and symptoms needed to determine a chemical culprit can be reduced to nearly 40 SSx without losing significant model accuracy. Evidence also suggests that the application of dimension reduction methods can improve ANN model performance accuracy.