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CSCD

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. © 2021 Journal of Clinical Otorhinolaryngology Head and Neck Surgery. All rights reserved.

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