Recurrence Prediction and Risk Classification of COPD Patients Based on Machine Learning

In response to the frequent recurrence and readmission of patients with chronic obstructive pulmonary disease, a machine learning based recurrence risk prediction and risk classification model for patients with chronic obstructive pulmonary disease is studied and constructed. Approach: This model first utilizes the optimized long short-term memory network to recognize named entities in patient electronic medical records and extract entity features. Then, XGBoost is used to predict the probability of patient relapse and readmission, and its risk is classified. Results: These results confirm that the optimized bidirectional long short-term memory network has the best performance with an accuracy of 84.36% in electronic medical record named entity recognition. The accuracy of XGBoost is the highest on both the training and testing sets, with values of 0.8827 and 0.8514, respectively. XGBoost has the best predictive ability and effectiveness. By using k-means for layering, the workload of manual evaluation was reduced by 91%, and the overall simulation accuracy of the model was as high as 97.3% and 96.4%. Conclusions: These indicate that this method can be used to balance high -risk patients between risk, cost, and resources.