Heterogeneity analysis of acute exacerbations of chronic obstructive pulmonary disease and a deep learning framework with weak supervision and privacy protection

Abstract

11.1 BackgroundChronic obstructive pulmonary disease (COPD) affects 5-10% of the adult US population and is a major cause of mortality. Acute exacerbations of COPD (AECOPDs) are a major driver of COPD morbidity and mortality, but there are no cost-effective methods to identify early AECOPDs when treatment is most likely to reduce the severity and duration of AECOPDs. 1.2 MethodsWe conducted the first long-term (> 12 months), real-time monitoring studies of AECOPD with wearable sensors and self-reporting. We applied a deep learning-based autoencoder for feature extractions, then applied K-means clustering to detect heterogeneity. Accordingly, we proposed a weakly supervised active learning framework to develop anomaly detection models for robust identification of early AECOPD, and a clustered federated learning approach to personalize the anomaly detection models for early detection of heterogeneous subtypes of AECOPD. We evaluated this model by comparing it with other unsupervised learning models and federated learning models. 1.3 FindingsWe identified two clusters based on the Silhouette score and SHAP analysis.One cluster shows high heart rate, low calories, and low steps; and the other has opposite characteristics. We also found out that a single subject could have exacerbation events from both clusters, indicating that there is not only subject-level heterogeneity but also event-level heterogeneity. Our weakly supervised framework outperformed unsupervised methods by 0.06 in average precision with 25 human annotation labels per subject. Our federated learning framework outperformed standard federated learning methods by 0.14 in F1 score and 0.17 in average precision. 1.4 InterpretationWe showed subject-level and event-level heterogeneity in AECOPD using mobile and wearable device data and developed a practical AECOPD detection framework with limited human annotated labels and keeping data private in each device.