Tentative diagnosis prediction via deep understanding of patient narratives A tentative diagnosis is a preliminary suspicion of patient status, which is usually made by physicians according to patient narrative right at admission. It largely depends on the experiences and professional knowledge of physicians. We explored a combination model for automatic tentative diagnosis prediction based on clinical narratives. Text features are extracted in two ways. Firstly, the context semantic features are extracted by attentionbased bidirectional longshort term memory (BiLSTM) network. Secondly, the symptom concepts recognized from input texts by Metamap and are vectorized by TFIDF. Two combination strategies are proposed to utilize both two features for one candidate international classification of diseases (ICD) code recommendation: feature vectors combination and prediction results combination. The experiments performed on MIMIC III dataset. Both of the two combination strategies achieved better performance, comparing with either of the model based on single type feature.

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.