Medical entity recognition of Esophageal Carcinoma based on word clustering Electronic Medical Records (EMRs) is the core of medical information system in hospital. EMRs arises from the medical institution, and large amount of clinical data generated every day. Due to the EMRs contains many medical entities and clinical information of the patient, by analyzing and mining the texts data, medical knowledge which closely related to patients or certain diseases can be obtained. In this paper, we ues the EMRs of patients with Esophageal Carcinoma(EC), which include the clinical symptoms of the patients, tests they have undergone, results of the examinations, and the diagnosis and treatment plan. In this paper, word vector training is carried out for largescale electronic medical record corpus by means of the skip model of word2vec deep learning tool. Then applying the features data to the kmeans clustering algorithm to identifying medical entities about EC by word clustering. As the first step of medical knowledge mining, medical entity recognition is the premise of extracting the semantic relationship implied in medical text and structuring EMRs.

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.