Artificial intelligence to organize patient portal messages: a journey from an ensemble deep learning text classification to rulebased named entity recognition Since the turn of the millennium, numerous healthcare venues all over the world have made a standard of communication transition from the classic telephone call to a sophisticated online patient portal system. More and more, a majority of patients prefer using portalstyle communication for clinician contact, checking lab results, and other informational transactions, in which hundreds of thousands of patient portal messages (PPMs) are daily generated as freetext data with multiple requests often buried in one single message. Thus, there is a pressing need to design and implement artificial intelligence (AI) algorithms to accurately organize this wealth of data in a timely fashion. With the present contribution, an attempt was made to first develop an ensemble deep learning text classification component and then integrate it with rulebased named entity recognition to categorize freetext PPMs submitted under the xe2x80x9cNonUrgent Medical Questionxe2x80x9d subject in the patient portal as either containing active symptom descriptions or logistical requests (e.g., appointment rescheduling).

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.