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Named-entity recognition (NER) is the application of information extraction by artificial intelligence (AI) to locate and classify conceptual entities from natural language into pre-defined categories. In this study, we apply the Long Short-Term Memory network (LSTM) networks to identify the patient entities from the Enterprise Master Patient Index (EMPI). A sample dataset with 300,000 deidentified patient records is used to test the LSTM performance for EMPI entity recognition. The data entries are firstly converted into strings and represented by a Word2Vec model with 200 dimensions. Two LSTM models are developed for the NER recognition problem. The first LSTM model uses a multiclassifier with a softmax function, the second LSTM model uses a two-step classification procedure by binary logistic function. To evaluate the LSTM performance, we use a conventional deep neural network model for comparison, where the Levenshtein distance is used to represent the training data patterns. The classification performance is evaluated by ten-fold cross-validation. The two-step LSTM model has the classification accuracy of 99.82%, which is superior to both the multi-classification LSTM classifier at 61.08% and to the conventional deep neural network at 95.08%. Therefore, we conclude that the new two-step LSTM model provides an accurate and reliable solution to recognize the EMPI patient entities when it is properly configured and trained.

Enterprise Master Patient Index Entity Recognition by Long Short-Term Memory Network in Electronic Health Systems