Traditional in-hospital rehabilitation treatment for chronic obstructive pulmonary disease (COPD) has problems such as resource shortage, high cost, and inconvenient transportation. To improve the convenience of rehabilitation treatment for COPD, a clinical data-driven intelligent diagnosis and treatment model is proposed for home rehabilitation management of COPD. The intelligent diagnosis and treatment model for home rehabilitation of COPD is optimized by combining the XGBoost algorithm and the long short-term memory network. The outcomes indicate that the XGBoost algorithm has the highest accuracy in processing clinical structured data, while the random forest (RF) algorithm has the lowest accuracy in processing clinical structured data. When the quantity of training samples is 300, the accuracy rates are 98% and 83%, respectively. The integration of the XGBoost algorithm and the long short-term memory network is used to process the monitoring indicators, resulting in the highest improvement rate and the smallest mean square error. When the sample size is 1000, the improvement rate of monitoring indicators is 42.5%, and the mean square error is 0.041. The method proposed in the study can effectively process clinical data, improve the accuracy of data processing, and accurately predict future changes in COPD.

Traditional in-hospital rehabilitation treatment for chronic obstructive pulmonary disease (COPD) has problems such as resource shortage, high cost, and inconvenient transportation. To improve the convenience of rehabilitation treatment for COPD, a clinical data-driven intelligent diagnosis and treatment model is proposed for home rehabilitation management of COPD. The intelligent diagnosis and treatment model for home rehabilitation of COPD is optimized by combining the XGBoost algorithm and the long short-term memory network. The outcomes indicate that the XGBoost algorithm has the highest accuracy in processing clinical structured data, while the random forest (RF) algorithm has the lowest accuracy in processing clinical structured data. When the quantity of training samples is 300, the accuracy rates are 98% and 83%, respectively. The integration of the XGBoost algorithm and the long short-term memory network is used to process the monitoring indicators, resulting in the highest improvement rate and the smallest mean square error. When the sample size is 1000, the improvement rate of monitoring indicators is 42.5%, and the mean square error is 0.041. The method proposed in the study can effectively process clinical data, improve the accuracy of data processing, and accurately predict future changes in COPD.

Traditional in-hospital rehabilitation treatment for chronic obstructive pulmonary disease (COPD) has problems such as resource shortage, high cost, and inconvenient transportation. To improve the convenience of rehabilitation treatment for COPD, a clinical data-driven intelligent diagnosis and treatment model is proposed for home rehabilitation management of COPD. The intelligent diagnosis and treatment model for home rehabilitation of COPD is optimized by combining the XGBoost algorithm and the long short-term memory network. The outcomes indicate that the XGBoost algorithm has the highest accuracy in processing clinical structured data, while the random forest (RF) algorithm has the lowest accuracy in processing clinical structured data. When the quantity of training samples is 300, the accuracy rates are 98% and 83%, respectively. The integration of the XGBoost algorithm and the long short-term memory network is used to process the monitoring indicators, resulting in the highest improvement rate and the smallest mean square error. When the sample size is 1000, the improvement rate of monitoring indicators is 42.5%, and the mean square error is 0.041. The method proposed in the study can effectively process clinical data, improve the accuracy of data processing, and accurately predict future changes in COPD.