Disease Prediction Model Based on BiLSTM and Attention Mechanism Electronic health records are digital records of patientsu0027 medical history, diagnosis, medication, treatment plans. EHRs not only contain the patientsu0027 medical and treatment history, but also systematically collect patientsu0027 clinical data. Therefore, it is very valuable to improve the patientu0027s 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 patientsu0027 hospitalization period are extracted from the MIMICIII database. Then, the extracted features are used to generate the patientsu0027 representation vector. Finally, we propose a prediction model based on BiLSTM and attention mechanism, which is called BiAttention. The BiLSTM is adopted to learn the forward and backward timing information in the patientu0027s representation vectors and to predict the patientu0027s 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.

Predictive Multilevel Patient Representations from Electronic Health Records The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records, physiological indicators, etc, which can be used for clinical outcome prediction tasks to support constructions of intelligent health systems. Learning patient representation from these clinical events for the clinical outcome prediction is an important but challenging step. Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction. However, clinical event sequence contains thousands of event types and temporal dependencies. We further make an observation that clinical events occurring in a short period are not constrained by any temporal order but events in a long term are influenced by temporal dependencies. The multiscale temporal property makes it difficult for traditional sequential models to capture the shortterm cooccurrence and the longterm temporal dependencies in clinical event sequences. In response to the above challenges, this paper proposes a Multilevel Representation Model (MRM). MRM first uses a sparse attention mechanism to model the shortterm cooccurrence, then uses intervalbased event pooling to remove redundant information and reduce sequence length and finally predicts clinical outcomes through Long ShortTerm Memory (LSTM). Experiments on realworld datasets indicate that our proposed model largely improves the performance of clinical outcome prediction tasks using EHR data.