A-BBL: A Risk Prediction Model for Patient Readmission based on Electronic Medical Records

Abstract

With the spread of medical digitization, electronic health record data has been accumulated in large quantities, laying the foundation for intelligent medical changes. ICU data is mined and analyzed to identify the risk of patient readmission in a timely manner, prevent and control the deterioration of patients' conditions, and reduce the burden of patient costs. However, due to the poor quality of medical data, potential information cannot be effectively mined. In view of the above problems, a patient readmission risk prediction model A-BBL is proposed. By extracting and analyzing the patient 's discharge summary information, the readmission risk of discharged patients within 30 days is predicted. The A-BBL model consists of three parts: firstly, BioBert is used to pre-train the medical text data, extract the semantic information of the medical text, and then generate the corresponding word vector. Then, the sequence model BiLSTM is used to capture the context information and model the input sequence. Finally, the self-attention mechanism is used to extract the key information in the input sequence, enhance the vector representation ability of the sequence, thereby improving the performance and accuracy of the model, so as to predict the readmission rate of patients. Based on the MIMIC-III real medical data set, the A-BBL model for patient readmission prediction proposed in this paper is verified. Compared with the baseline model, the accuracy is improved by 7.2 %. This study can help medical staff better understand and pay attention to the progression of critically ill patients, im-prove the survival rate of patients, and reduce the readmission rate of patients.