融合空气数据的深度网络慢阻肺评估测试评分预测模型的构建及意义  

Construction and significance of prediction model for chronic obstructive pulmonary disease assessment test based on fusion deep network fused with air data

作　　者：孙婉璐 张迎春 杜富瑞 周号益 张荣葆 王卓 李建欣 陈亚红[1] Sun Wanlu;Zhang Yingchun;Du Furui;Zhou Haoyi;Zhang Rongbao;Wang Zhuo;Li Jianxin;Chen Yahong(Department of Respiratory and Critical Care Medicine,Peking University Third Hospital,Beijing 100191,China;Department of Respiratory and Critical Care Medicine,Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital,Capital Medical University,Beijing 100020,China;School of Computer Science and Engineering,Beihang University,Beijing 100191,China;Beijing General Research Institute of Mining&Metallurgy Technology Group,State Key Laboratory of Process Automation in Mining and Metallurgy,Beijing 100044,China;Department of Respiratory and Critical Care Medicine,Peking University People′s Hospital,Beijing 100044,China)

机构地区：[1]北京大学第三医院呼吸与危重症医学科,北京100191 [2]首都医科大学附属北京朝阳医院北京市呼吸疾病研究所呼吸与危重症医学科,北京100020 [3]北京航空航天大学计算机学院,北京100191 [4]北京矿冶研究总院矿冶过程自动控制技术国家重点实验室,北京100044 [5]北京大学人民医院呼吸与危重症医学科,北京100044

出　　处：《中华健康管理学杂志》2022年第10期721-727,共7页Chinese Journal of Health Management

基　　金：国家自然科学基金重大项目课题(82090014);首都卫生发展科研专项项目(首发2020-2Z-40917);2020—2021年度北京大学第三医院队列建设项目(BYSYDL2021013)。

摘　　要：目的构建融合空气数据的深度网络慢性阻塞性肺疾病(慢阻肺)评估测试(CAT)评分预测模型,并探讨其意义。方法采用定组研究的方法,自2015年2月至2017年12月从北京大学第三医院、北京大学人民医院和北京积水潭医院呼吸科门诊入选稳定期的慢阻肺患者,采集患者住宅区附近的室外环境监测空气数据,计算患者每日空气污染物暴露量和天气数据,并连续记录患者每日的CAT评分。通过融合时序算法和神经网络建立模型对患者未来一周的CAT评分进行预测,并比较该模型与长短期记忆模型(LSTM)、添加全局注意力机制的LSTM模型(LSTM-attention)和自回归移动模型(ARIMA)的预测准确程度,并探讨预测模型的意义。结果共纳入47例慢阻肺患者,平均随访时间为381.60 d,利用采集的空气数据和CAT评分构建长短期记忆模型-卷积神经网络-自回归(LSTM-CNN-AR)模型,其模型的均方根误差为0.85,平均绝对误差为0.71,较LSTM、LSTM-attention和ARIMA三者中最优模型平均预测准确度提升21.69%。结论基于慢阻肺患者所处环境的空气数据,融合深度网络模型可更精准地预测慢阻肺患者的CAT评分。Objective To construct a chronic obstructive pulmonary disease(COPD)assessment test(CAT)score prediction model based on a deep network fused with air data,and to explore its significance.Methods From February 2015 to December 2017,the outdoor environmental monitoring air data near the residential area of the patients with COPD from the Respiratory Outpatient Clinics of Peking University Third Hospital,Peking University People′s Hospital and Beijing Jishuitan Hospital were collected and the daily air pollution exposure of patients was calculated.The daily CAT scores were recorded continuously.The CAT score of the patients in the next week was predicted by fusing the time series algorithm and neural network to establish a model,and the prediction accuracy of the model was compared with that of the long short-term memory model(LSTM),the LSTM-attention model and the autoregressive integrated moving average model(ARIMA).Results A total of 47 patients with COPD were enrolled and followed up for an average of 381.60 days.The LSTM-convolutional neural networks(CNN)-autoregression(AR)model was constructed by using the collected air data and CAT score,and the root mean square error of the model was 0.85,and the mean absolute error was 0.71.Compared with LSTM,LSTM-attention and ARIMA,the average prediction accuracy was improved by 21.69%.Conclusion Based on the air data in the environment of COPD patients,the fusion deep network model can predict the CAT score of COPD patients more accurately.

关 键 词：肺疾病 慢性阻塞性 空气污染 深度网络模型 计算机分析 

分 类 号：R563.9[医药卫生—呼吸系统]