Disease Prediction Model Based on BiLSTM and Attention Mechanism

Electronic health records are digital records of patients' medical history, diagnosis, medication, treatment plans. EHRs not only contain the patients' medical and treatment history, but also systematically collect patients' clinical data. Therefore, it is very valuable to improve the patient's health care management by mining the information in the EHRs. However, due to the irregularities and sparsity of EHRs, EHRs mining is very challenging. In this paper, the laboratory data, physiological indicators and diagnosis time during the patients' hospitalization period are extracted from the MIMIC-III database. Then, the extracted features are used to generate the patients' representation vector. Finally, we propose a prediction model based on BiLSTM and attention mechanism, which is called Bi-Attention. The BiLSTM is adopted to learn the forward and backward timing information in the patient's representation vectors and to predict the patient's disease by utilizing the specific clinical information in the timed medical record with the attention mechanism. The experimental results show that compared with other methods, the proposed model can effectively improve the prediction performance.