BEHRT: Transformer for Electronic Health Records
Open accessAbstract
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).
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BEHRT is limited to forecasts of disorders occurring in the next patient hospital visit or a specific predefined timeframe (eg, 6 or 12 months), consequently requiring that the information is grouped into patient visits.
“BEHRT is limited to forecasts of disorders occurring in the next patient hospital visit or a specific predefined timeframe (eg, 6 or 12 months), consequently requiring that the information is grouped into patient visits.”
From Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study (2024) · cites this paper as [4]
Methods > Disease Prediction describes the three evaluated forecasts—diagnoses at the next visit, in six months, and in twelve months. Methods > Pre-processing of CPRD represents records as sequences of visits, but those visits include both GP and hospital visits. The paper does not establish that BEHRT is limited to these forecasts; its Discussion proposes other downstream tasks. (LLM, conf=0.88)
Experimental — accuracy not yet measuredBEHRT uses a small subset of disorders (301 in total) that were available in the structured portion of EHRs.
“One example is the BERT for EHRs (BEHRT) model, 4 which uses a small subset of disorders (301 in total) that were available in the structured portion of EHRs.”
From Foresight-a generative pretrained transformer for modelling of patient timelines using electronic health records: a retrospective modelling study (2024) · cites this paper as [4]
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- Authors
- Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G.
- Affiliations
- University of Oxford (GB)
- Corresponding author
- Rao, Shishir
- ORCID
- Venue
- Scientific Reports
- Published
- 2020-04-28
- Volume
- 10
- Issue
- 1
- Pages
- 7155-7155
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7189231
- OpenAlex
- W3017637887
- Cited by
- 595
- References
- 37
- Citations by year
- 2026: 129 · 2025: 128 · 2024: 106 · 2023: 98 · 2022: 60 · 2021: 58 · 2020: 16
- FWCI
- 22.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UNSWorks (University of New South Wales, Sydney, Australia) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- DH | National Institute for Health Research
- Oxford University | Oxford Martin School, University of Oxford
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- electronic health records, disease prediction, early disease detection, sequence-to-sequence models, transformer models, multi-task prediction, heterogeneous clinical data, interpretable prediction, patient representation learning, transfer learning
- MeSH terms
- Machine Learning, Algorithms, Humans, Electronic Health Records
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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