Readmission Prediction for Patients with Chronic Obstructive Pulmonary Disease Based on Machine Learning

Due to the high recurrence rate of chronic obstructive pulmonary disease (COPD), the issue of unplanned readmissions has become a significant challenge for patients. In this research, a framework and a methodology are proposed that integrate different structured data and multiple machine learning algorithms for risk prediction. A method is showed using genuine electronic medical information from approximately 10 000 COPD patients at a tertiary hospital in Guangzhou, China. To handle unstructured input, a Bidirectional Long Short-Term Memory-Conditional Random Field (BiLSTM-CRF) model known as named entity recognition is used. Furthermore, risk prediction models with Support Vector Machines (SVM), Random Forests (RF), Extreme Gradient Boosting (XGBoost), and Back Propagation (BP) Neural Network are developed. The results show that the XGBoost model performs best. The length of hospital stay, Charlson Comorbidity Index, disease duration, white blood cell count, and eosinophil count are also identified as the most relevant predictors for readmission. An understanding of chronic diseases is advanced by providing research insights and decision support tools for early detection, prompt diagnosis, and precise intervention.