Construction and significance of prediction model for chronic obstructive pulmonary disease assessment test based on fusion deep network fused with air data
10.3760/cma.j.cn115624-20220601-00425
- VernacularTitle: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
- Author:
Wanlu SUN
1
;
Yingchun ZHANG
;
Furui DU
;
Haoyi ZHOU
;
Rongbao ZHANG
;
Zhuo WANG
;
Jianxin LI
;
Yahong CHEN
Author Information
1. 北京大学第三医院呼吸与危重症医学科，北京 100191
- Keywords:
Pulmonary disease, chronic obstructive;
Air pollution;
Deep network model;
Computer analysis
- From:
Chinese Journal of Health Management
2022;16(10):721-727
- CountryChina
- Language:Chinese
-
Abstract:
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.
