A machine learning model for predicting acute exacerbation of in-home chronic obstructive pulmonary disease patients

Purpose: This study utilized intelligent devices to remotely monitor patients with chronic obstructive pulmonary disease (COPD), aiming to construct and evaluate machine learning (ML) models that predict the probability of acute exacerbations of COPD (AECOPD). Methods: Patients diagnosed with COPD Group C/D at our hospital between March 2019 and June 2021 were enrolled in this study. The diagnosis of COPD Group C/D and AECOPD was based on the GOLD 2018 guidelines. We developed a series of machine learning (ML)-based models, including XGBoost, LightGBM, and CatBoost, to predict AECOPD events. These models utilized data collected from portable spirometers and electronic stethoscopes within a five-day time window. The area under the ROC curve (AUC) was used to assess the effectiveness of the models. Results: A total of 66 patients were enrolled in COPD groups C/D, with 32 in group C and 34 in group D. Using observational data within a five-day time window, the ML models effectively predict AECOPD events, achieving high AUC scores. Among these models, the CatBoost model exhibited superior performance, boasting the highest AUC score (0.9721, 95 % CI: 0.9623-0.9810). Notably, the boosting tree methods significantly outperformed the time-series based methods, thanks to our feature engineering efforts. A post-hoc analysis of the CatBoost model reveals that features extracted from the electronic stethoscope (e.g., max/min vibration energy) hold more importance than those from the portable spirometer. Conclusions: The tree-based boosting models prove to be effective in predicting AECOPD events in our study. Consequently, these models have the potential to enhance remote monitoring, enable early risk assessment, and inform treatment decisions for homebound patients with chronic COPD.