Patient Entity Recognition by Automatic EHR Context Understanding and Deep Learning Patient entity recognition or patient entity extraction is to detect the relevant electronic health records (EHRs) across multiple data sources belonging to an identical patient, and to link the relevant data together. Patient entity recognition is a useful technology for crosssystem electronic health data analysis to define commonality, to synthesize multiple data sources, and to reduce data redundancy. In this paper, we propose a deep learning solution, a sequential LSTM  Word Embedding network model (WE  LSTM) to filter and represent the nonstructured electronic health records by measuring their context similarity and link them to the identical patient entities. The text context features are at first filtered by a trained bidirectional LSTM network to filter the irrelevant patient entities, and the related patient information context is estimated by a trained shallow word embedding network for its word vector similarity with the existing entities in the database. Finally, the new input patient data will be linked to the existing patient entity in the dataset with the greatest context similarity. Our hypothesis is that the records pointing to the identical patient have closest context similarity, so the patterns can be encoded by a trained word embedding network. An infection disease registration dataset (5304 patient entities) is used to evaluate the performance of the proposed WELSTM model. The classification accuracy is 0.837 and the F score is 0.843, which is the highest compared to the comparators including a single word embedding model, a random forest model, and a conventional neural network model. In addition, the WE  LSTM model has the greatest AUG area when the ROC of the four models are compared. This result indicates the proposed WE  LSTM model provides a feasible solution to correctly recognize the patient identities from electronic records by measure the text context similarity. It provides a solution for patient identity recognition through multisource health big data integration, which is an urgent task for health big data projects.

Combinatorial feature embedding based on CNN and LSTM for biomedical named entity recognition Abstract   With the rapid advancement of technology and the necessity of processing large amounts of data, biomedical Named Entity Recognition (NER) has become an essential technique for information extraction in the biomedical field. NER, which is a sequencelabeling task, has been performed using various traditional techniques including dictionary, rule, machine learning, and deep learningbased methods. However, as existing biomedical NER models are insufficient to handle new and unseen entity types from the growing biomedical data, the development of more effective and accurate biomedical NER models is being widely researched. Among biomedical NER models utilizing deep learning approaches, there have been only a few studies involving the design of highlevel features in the embedding layer. In this regard, herein, we propose a deep learning NER model that effectively represents biomedical word tokens through the design of a combinatorial feature embedding. The proposed model is based on Bidirectional Long ShortTerm Memory (biLSTM) with Conditional Random Field (CRF) and enhanced by integrating two different characterlevel representations extracted from a Convolutional Neural Network (CNN) and biLSTM. Additionally, an attention mechanism is applied to the model to focus on the relevant tokens in the sentence, which alleviates the longterm dependency problem of the LSTM model and allows effective recognition of entities. The proposed model was evaluated on two benchmark datasets, the JNLPBA and NCBIDisease, and a comparative analysis with the existing models is performed. The proposed model achieved a relatively higher performance with an F1score of 86.93% in case of NCBI Data, and a competitive performance for the JNLPBA with an F1score of 75.31%.