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Deep learning for electronic health records: A comparative review of multiple deep neural architectures
Open accessAbstract
Despite the recent developments in deep learning models, their applications in clinical decision-support systems have been very limited. Recent digitalisation of health records, however, has provided a great platform for the assessment of the usability of such techniques in healthcare. As a result, the field is starting to see a growing number of research papers that employ deep learning on electronic health records (EHR) for personalised prediction of risks and health trajectories. While this can be a promising trend, vast paper-to-paper variability (from data sources and models they use to the clinical questions they attempt to answer) have hampered the field's ability to simply compare and contrast such models for a given application of interest. Thus, in this paper, we aim to provide a comparative review of the key deep learning architectures that have been applied to EHR data. Furthermore, we also aim to: (1) introduce and use one of the world's largest and most complex linked primary care EHR datasets (i.e., Clinical Practice Research Datalink, or CPRD) as a new asset for training such data-hungry models; (2) provide a guideline for working with EHR data for deep learning; (3) share some of the best practices for assessing the "goodness" of deep-learning models in clinical risk prediction; (4) and propose future research ideas for making deep learning models more suitable for the EHR data. Our results highlight the difficulties of working with highly imbalanced datasets, and show that sequential deep learning architectures such as RNN may be more suitable to deal with the temporal nature of EHR.
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“It is usually a difficult task for the traditional sequence labeling algorithms to extract relevant information from EHR data [43]”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [43]
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“Some recent comparative studies such as Ayala Solares et al. [137], Sadati et al. [138], and Min et al. [139] provided some surprising observations in terms of how to choose the best representation learning methods.”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [137]
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Self-citation
Metadata
- Authors
- Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., Conrad, N., Rahimi, K., & Salimi-Khorshidi, G.
- Affiliations
- The George Institute for Global Health (GB)
- Corresponding author
- Zhu, Yajie
- ORCID
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 101
- Pages
- 103337
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W3000238064
- Cited by
- 247
- References
- 80
- Citations by year
- 2026: 23 · 2025: 36 · 2024: 37 · 2023: 72 · 2022: 37 · 2021: 29 · 2020: 13
- FWCI
- 14.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Newcastle University ePrints (Newcastle Univesity)
- Funders
- NIHR Oxford Biomedical Research Centre
- UK Research and Innovation
- Oxford Martin School, University of Oxford
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- electronic health records, deep learning architectures, clinical risk prediction, Clinical Practice Research Datalink, class imbalance, recurrent neural networks, temporal data, clinical decision support
- MeSH terms
- Deep Learning, Forecasting, Electronic Health Records
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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