Telesca Vito, Rondinone Maríca
Department of Engineering, University of Basilicata, 85100 Potenza, Italy.
Sensors (Basel). 2025 Aug 7;25(15):4864. doi: 10.3390/s25154864.
This study presents an approach based on machine learning (ML) techniques to analyze the relationship between emergency room (ER) admissions for cardiorespiratory diseases (CRDs) and environmental factors. The aim of this study is the development and verification of an interpretable machine learning framework applied to environmental and health data to assess the relationship between environmental factors and daily emergency room admissions for cardiorespiratory diseases. The model's predictive accuracy was evaluated by comparing simulated values with observed historical data, thereby identifying the most influential environmental variables and critical exposure thresholds. This approach supports public health surveillance and healthcare resource management optimization. The health and environmental data, collected through meteorological sensors and air quality monitoring stations, cover eleven years (2013-2023), including meteorological conditions and atmospheric pollutants. Four ML models were compared, with XGBoost showing the best predictive performance (R = 0.901; MAE = 0.047). A 10-fold cross-validation was applied to improve reliability. Global model interpretability was assessed using SHAP, which highlighted that high levels of carbon monoxide and relative humidity, low atmospheric pressure, and mild temperatures are associated with an increase in CRD cases. The local analysis was further refined using LIME, whose application-followed by experimental verification-allowed for the identification of the critical thresholds beyond which a significant increase in the risk of hospital admission (above the 95th percentile) was observed: CO > 0.84 mg/m, P_atm ≤ 1006.81 hPa, Tavg ≤ 17.19 °C, and RH > 70.33%. The findings emphasize the potential of interpretable ML models as tools for both epidemiological analysis and prevention support, offering a valuable framework for integrating environmental surveillance with healthcare planning.
本研究提出了一种基于机器学习（ML）技术的方法，用于分析心肺疾病（CRD）的急诊室（ER）入院情况与环境因素之间的关系。本研究的目的是开发并验证一个可解释的机器学习框架，该框架应用于环境与健康数据，以评估环境因素与心肺疾病每日急诊室入院情况之间的关系。通过将模拟值与观测到的历史数据进行比较，评估了模型的预测准确性，从而确定最具影响力的环境变量和关键暴露阈值。这种方法有助于公共卫生监测和医疗资源管理优化。通过气象传感器和空气质量监测站收集的健康与环境数据涵盖了十一年（2013 - 2023年），包括气象条件和大气污染物。比较了四种ML模型，其中XGBoost表现出最佳的预测性能（R = 0.901；平均绝对误差 = 0.047）。应用了10折交叉验证以提高可靠性。使用SHAP评估了全局模型可解释性，结果表明高浓度一氧化碳和相对湿度、低气压以及温和温度与CRD病例增加有关。使用LIME进一步细化了局部分析，其应用（随后进行实验验证）使得能够确定关键阈值，超过该阈值观察到入院风险显著增加（高于第95百分位数）：一氧化碳> 0.84毫克/立方米，大气压力≤ 1006.81百帕，平均温度≤ 17.19摄氏度，相对湿度> 70.33%。研究结果强调了可解释的ML模型作为流行病学分析和预防支持工具的潜力，为将环境监测与医疗规划相结合提供了一个有价值的框架。
