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BEHRT: Transformer for Electronic Health Records

Scientific reports, 2020-04, Vol.10 (1), p.7155-7155, Article 7155 [Peer Reviewed Journal]

The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2020 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-020-62922-y ;PMID: 32346050

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  • Title:
    BEHRT: Transformer for Electronic Health Records
  • Author: Li, Yikuan ; Rao, Shishir ; Solares, José Roberto Ayala ; Hassaine, Abdelaali ; Ramakrishnan, Rema ; Canoy, Dexter ; Zhu, Yajie ; Rahimi, Kazem ; Salimi-Khorshidi, Gholamreza
  • Subjects: Algorithms ; Electronic Health Records ; Electronic medical records ; Humans ; Learning algorithms ; Machine Learning ; Transfer learning
  • Is Part Of: Scientific reports, 2020-04, Vol.10 (1), p.7155-7155, Article 7155
  • Description: Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one's future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0-13.2% (in terms of average precision scores for different tasks), over the existing state-of-the-art deep EHR models. In addition to its scalability and superior accuracy, BEHRT enables personalised interpretation of its predictions; its flexible architecture enables it to incorporate multiple heterogeneous concepts (e.g., diagnosis, medication, measurements, and more) to further improve the accuracy of its predictions; its (pre-)training results in disease and patient representations can be useful for future studies (i.e., transfer learning).
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-020-62922-y
    PMID: 32346050
  • Source: PubMed Central (Open access)
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