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

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      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Entity recognition, Long short-term memory (LSTM), Deep learning, Machine learning

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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 multi-classifier 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.

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          Most cited references 4

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          Developing a Common Health Information Exchange Platform to Implement a Nationwide Health Information Network in South Korea

          Objectives We aimed to develop a common health information exchange (HIE) platform that can provide integrated services for implementing the HIE infrastructure in addition to guidelines for participating in an HIE network in South Korea. Methods By exploiting the Health Level 7 (HL7) Clinical Document Architecture (CDA) and Integrating the Healthcare Enterprise (IHE) Cross-enterprise Document Sharing-b (XDS.b) profile, we defined the architectural model, exchanging data items and their standardization, messaging standards, and privacy and security guidelines, for a secure, nationwide, interoperable HIE. We then developed a service-oriented common HIE platform to minimize the effort and difficulty of fulfilling the standard requirements for participating in the HIE network. The common platform supports open application program interfaces (APIs) for implementing a document registry, a document repository, a document consumer, and a master patient index. It could also be used for testing environments for the implementation of standard requirements. Results As the initial phase of implementing a nationwide HIE network in South Korea, we built a regional network for workers' compensation (WC) hospitals and their collaborating clinics to share referral and care record summaries to ensure the continuity of care for industrially injured workers, using the common HIE platform and verifying the feasibility of our technologies. Conclusions We expect to expand the HIE network on a national scale with rapid support for implementing HL7 and IHE standards in South Korea.
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            Word representations: A simple and general method for semi-supervised learning

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              Entity recognition in the biomedical domain using a hybrid approach

              Background This article describes a high-recall, high-precision approach for the extraction of biomedical entities from scientific articles. Method The approach uses a two-stage pipeline, combining a dictionary-based entity recognizer with a machine-learning classifier. First, the OGER entity recognizer, which has a bias towards high recall, annotates the terms that appear in selected domain ontologies. Subsequently, the Distiller framework uses this information as a feature for a machine learning algorithm to select the relevant entities only. For this step, we compare two different supervised machine-learning algorithms: Conditional Random Fields and Neural Networks. Results In an in-domain evaluation using the CRAFT corpus, we test the performance of the combined systems when recognizing chemicals, cell types, cellular components, biological processes, molecular functions, organisms, proteins, and biological sequences. Our best system combines dictionary-based candidate generation with Neural-Network-based filtering. It achieves an overall precision of 86% at a recall of 60% on the named entity recognition task, and a precision of 51% at a recall of 49% on the concept recognition task. Conclusion These results are to our knowledge the best reported so far in this particular task.

                Author and article information

                July 2018
                July 2018
                : 1-4
                York University

                4700 Keele St., Toronto, Canada
                Guangzhou Univ Chinese Med

                111 Dade Rd, Guangzhou, China
                Dapasoft INC

                111 Gordon Baker Rd, Toronto, Canada
                © Liang et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Electronic Workshops in Computing


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