摘要
目的探究基于实时室内空气环境监测数据与机器学习算法的医院室内空气微生物浓度预测。方法选取2022年5月23日—6月5日某院四个位置为监测采样点,采用物联网传感器实时监测多种空气环境数据,匹配各点位采集的空气微生物浓度数据,使用梯度提升树算法(GBDT)对医院室内空气微生物浓度进行实时预测,并选取其他五种常见的机器学习模型进行比较,对比模型包括随机森林(RF)、决策树(DT)、最近邻(KNN)、线性回归(LR)和人工神经网络(ANN)。最后通过平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)三个指标验证模型的有效性。结果GBDT模型在门诊电梯间(A点)、支气管镜诊间(B点)、CT候诊区(C点)和供应室护士站(D点)的MAPE值分别为22.49%、36.28%、29.34%、26.43%,GBDT模型在三个采样点的平均性能高于其他机器学习模型,仅在一个采样点略低于ANN模型。GBDT模型在四个点位的平均MAPE值为28.64%,即预测值偏离实际值28.64%,说明GBDT模型预测结果较好,预测值在可用范围内。结论基于实时室内空气环境监测数据的GBDT机器学习模型能够提高医院室内空气微生物浓度预测精度。
Objective To explore the prediction of hospital indoor microbial concentration in air based on real-time indoor air environment monitoring data and machine learning algorithms.Methods Four locations in a hospital were selected as monitoring sampling points from May 23 to June 5,2022.The“internet of things”sensor was used to monitor a variety of real-time air environment data.Air microbial concentration data collected at each point were matched,and the gradient boosting decision tree(GBDT)was used to predict real-time indoor microbial concentration in air.Five other common machine learning models were selected for comparison,including random forest(RF),decision tree(DT),k-nearest neighbor(KNN),linear regression(LR)and artificial neural network(ANN).The validity of the model was verified by the mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE).Results The MAPE value of GBDT model in the outpatient elevator room(point A),bronchoscopy room(point B),CT waiting area(point C),and nurses’station in the supply room(point D)were 22.49%,36.28%,29.34%,and 26.43%,respectively.The mean performance of the GBDT model was higher than that of other machine learning models at three sampling points and slightly lower than that of the ANN model at only one sampling point.The mean MAPE value of GBDT model at four sampling points was 28.64%,that is,the predicted value deviated from the actual value by 28.64%,indicating that GBDT model has good prediction results and the predicted value was within the available range.Conclusion The GBDT machine learning model based on real-time indoor air environment monitoring data can improve the prediction accuracy of indoor air microbial concentration in hospitals.
作者
杨光飞
邬水
钱翔宇
杨宇红
孙野
邹韵
庚俐莉
刘媛
YANG Guang-fei;WU Shui;QIAN Xiang-yu;YANG Yu-hong;SUN Ye;ZOU Yun;GENG Li-li;LIU Yuan(Central Hospital of Dalian University of Technology,Dalian 116000,China;Institute of Systems Engineering,Dalian University of Technology,Dalian 116024,China;School of Environmental Science and Technology,Dalian University of Technology,Dalian 116024,China;The Retired-serving Department,Cancer Hospital of Dalian University of Technology,Shenyang 110042,China;Office of Disease Prevention and Infection Control,Cancer Hospital of Dalian University of Technology,Shenyang 110042,China;Teaching and Student Affairs Department,Cancer Hospital of Dalian University of Technology,Shenyang 110042,China;Department of Infectious Diseases,Central Hospital of Dalian University of Technology,Dalian 116000,China;Department of Pulmonary and Critical Medicine,Central Hospital of Dalian University of Technology,Dalian 116000,China)
出处
《中国感染控制杂志》
CAS
CSCD
北大核心
2024年第7期787-797,共11页
Chinese Journal of Infection Control
基金
国家自然科学基金面上项目(42071273)。
关键词
微生物浓度
室内环境
GBDT模型
空气微生物浓度
microbial concentration
indoor environment
GBDT model
air microbial concentration
