Practice of distributed machine learning in clinical modeling for chronic obstructive pulmonary disease.

Background: The high prevalence, morbidity and mortality, and disease heterogeneity of chronic obstructive pulmonary disease (COPD) result in the scattered data derived from patient visits in different medical units. The huge cost of integrating the scattered data for analysis and modeling, as well as the legal demand for patient privacy protection lead to the emergence of data island.

Objectives: On the premise of protecting patient privacy, integrating scattered data of patients from different medical units for high-quality modeling is beneficial to promoting the development of digital health. Based on this, we develop a distributed COPD disease diagnosis system termed COPD average federated learning (COPD_AVG_FL) using FedAvg.

Methods: First, to build the COPD_AVG_FL, the clinical data of COPD patients from the real world is collected and the data pre-processing is performed to clean the incorrect data, outlier samples and missing values. Then, a classical federated learning architecture is designed as COPD_AVG_FL. Finally, to evaluate the established COPD_AVG_FL system, we develop Centralized Machine Learning (CML).

Conclusions: Our results suggest that, with the assistance of COPD_AVG_FL, the absolute improvement rates are 13.4% (accuracy), 13.3% (precision), 12.8% (recall), 13.1% (F1-Score) and 12.9% (AUC) on the test data, respectively. The decoupling between model training and raw training data protects the patients' privacy, and helps to securely integrate more COPD data from different medical units to generate a more comprehensive model COPD_AVG_FL. This approach promotes the landing of wise information technology of medicine for COPD in the real clinical world. Code for our model will be made available at https://github.com/Cczhh/COPD_AVG_FL/tree/master.