论文标题
在化学质量伤亡事件中，在人工神经网络中降低尺寸降低以改善急诊室分流
Application of Dimensional Reduction in Artificial Neural Networks to Improve Emergency Department Triage During Chemical Mass Casualty Incidents
论文作者
论文摘要
化学大规模伤亡事件（MCI）给医院工作人员和资源带来了沉重的负担。机器学习（ML）工具可以为护理人员提供有效的决策支持。但是，ML模型需要大量数据才能获得最准确的结果，这在化学MCI的混乱性质中通常不可行。这项研究检查了四种统计维度降低技术的应用：随机选择，协方差/方差，Pearson的线性相关性和原理成分分析，以减少311种有害化学物质的数据集和79种相关符号和症状（SSX）。开发了人工神经网络管道来创建比较模型。结果表明，确定化学罪魁祸首所需的体征和症状数量可以减少到近40 ssx，而不会丢失显着的模型准确性。证据还表明，缩小方法的应用可以提高ANN模型性能准确性。
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.
