The DOI matches its Crossref record; 84 of the 84 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173
Glove: Global Vectors for Word Representation
Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Advances in Neural Information Processing Systems 25
Zhang, Y., Sutton, C. A., Storkey, A., & Zoubin Ghahramani, A. (2012) · Cited by 12,653
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
(2016) · Cited by 11,679
He, H., & Garcia, E. A. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.239 · Cited by 10,359
Research works printed but not linked: 14
- S. Ebadollahi, J. Sun, D. Gotz, J. Hu, D. Sow, C. Neti, Predicting patient’s trajectory of physiological data using temporal trends in similar patients: a system for near-term prognostics, in: AMIA Annual Symposium Proceedings, Vol. 2010, American Medical Informatics Association, 2010, p. 192.
- R.J. Carroll, A.E. Eyler, J.C. Denny, Naïve electronic health record phenotype identification for rheumatoid arthritis, in: AMIA Annual Symposium Proceedings, Vol. 2011, American Medical Informatics Association, 2011, p. 189.
- T. Yue, H. Wang, Deep Learning for Genomics: A Concise Overview, arXiv.
- E. Choi, M.T. Bahadori, J. Sun, J. Kulas, A. Schuetz, W. Stewart, RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism, in: Advances in Neural Information Processing Systems, 2016, pp. 3504–3512.
- E. Choi, M.T. Bahadori, A. Schuetz, W.F. Stewart, J. Sun, Doctor AI: Predicting Clinical Events via Recurrent Neural Networks, in: Proceedings of the 1st Machine Learning for Healthcare Conference, Vol. 56 of Proceedings of Machine Learning Research, PMLR, 2016, pp. 301–318. <http://proceedings.mlr.press/v56/Choi16.html>.
- B.C. Kwon, M.-J. Choi, J.T. Kim, E. Choi, Y.B. Kim, S. Kwon, J. Sun, J. Choo, RetainVis: visual analytics with interpretable and interactive recurrent neural networks on electronic medical records, IEEE Trans. Visual. Comput. Graph.
- L. Buitinck, G. Louppe, M. Blondel, F. Pedregosa, A. Mueller, O. Grisel, V. Niculae, P. Prettenhofer, A. Gramfort, J. Grobler, R. Layton, J. VanderPlas, A. Joly, B. Holt, G. Varoquaux, API design for machine learning software: experiences from the scikit-learn project, in: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 2013, pp. 108–122.
- M. Pelikan, D.E. Goldberg, E. Cantú-Paz, BOA: The Bayesian Optimization Algorithm, in: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation - Volume 1, GECCO’99, Morgan Kaufmann Publishers Inc., 1999, pp. 525–532.
- Fawcett, 2004, ROC graphs: notes and practical considerations for researchers, Mach. Learn., 31, 1
- J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M.M.A. Patwary, Prabhat, R.P. Adams, Scalable Bayesian Optimization Using Deep Neural Networks, in: ICML, 2015.
- J.T. Springenberg, A. Klein, S. Falkner, F. Hutter, Bayesian Optimization with Robust Bayesian Neural Networks, in: NIPS, 2016.
- Y. Gal, Uncertainty in Deep Learning, University of Cambridge.
- A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L. Kaiser, I. Polosukhin, Attention Is All You Need, in: NIPS, 2017.
- S. Lee, Natural language generation for electronic health records, Comput. Res. Reposit., abs/1806.01353. · arXiv
Cited by
Sorted by citations
Items 201–246 of 246
VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models.Preprint
Cheng, F., Liu, D., Du, F., Lin, Y., Zytek, A., Li, H., Qu, H., & Veeramachaneni, K. K. (2021) · arXiv (Cornell University)
Park, Y., Joe, B., Hamm, J., Shin, I., & Lee, J. (2022) · doi:10.2196/preprints.38440
A REVIEW ON MACHINE LEARNING TECHNIQUES FOR ADVANCED HEALTH CARE SYSTEMS
(2020) · International Journal of Engineering Sciences & Research Technology · doi:10.29121/ijesrt.v9.i11.2020.1
Applications of Bayesian Network for Medical and Clinical Data
Uchiyama, Y., Fujiki, S., Nakano, J., Aoyagi, K., Higuchi, Y., & Kansaku, K. (2022) · リハビリテーション医学 : 日本リハビリテーション医学会誌 · doi:10.2490/jjrmc.59.1100
Decision Support System for Weeding Out Drug Seeking Behavior from Emergency Clinics
Deolekar, R. V., Wankhade, S. B., & Wanve, M. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-7402-1_17
Novel Representation Learning Improves Personalizing Blood Test Ranges and Disease Risk PredictionPreprint
Heydari, A. A., Prieto, J. L., Patel, S., & Rezaei, N. (2023) · Research Square · doi:10.21203/rs.3.rs-3054397/v1
An Interactive Web Solution for Electronic Health Records Segmentation and Prediction
Mathew, S., Jayaprakash, M. D., & Agarwal, R. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-35078-8_8
Deborah, R. N., Alwyn Rajiv, S., Vinora, A., Priyadharshini, J. M. H., Priyanga, P., & Lakshmi, K. V. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10262918
Prabakaran, B. S., & Shafique, M. (2023) · Springer eBooks · doi:10.1007/978-3-031-40677-5_2
Machine learning and pre-exposure prophylaxis: A survey
Nyakanga, J. N. (2023) · GSC Biological and Pharmaceutical Sciences · doi:10.30574/gscbps.2023.25.3.0182
Data analytics framework for sparse longitudinal structured biomedical data
Sixtová, I., Skopal, T., & Uher, T. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385940
Predictions of Early Hospitalization of Diabetes Patients Based on Deep Learning: A Review
Al-Atroshi, C., & Abdulazeez, A. M. (2024) · Indonesian Journal of Computer Science · doi:10.33022/ijcs.v13i1.3738
Islam, H., Bartlett, G. B., Pierce, R., Rao, P. R., Waitman, L. R., & Song, X. (2024) · medRxiv · doi:10.1101/2024.04.15.24305843
Smita, & Kumar, E. (2024) · SN Computer Science · doi:10.1007/s42979-024-02946-7
Graph-Based Data Representation and Prediction in Medical Domain Tasks Using Graph Neural Networks
Sofiia, V., Derevitskii, I. V., Abramyan, L., & Vatian, A. S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-63772-8_32
Hua, Y., Li, M. L., Li, M. L., Mukkamala, A., Estrada, C. R., & Wang, H.-H. (2024) · doi:10.2196/preprints.63745
Mapping the Clinical Pathway for Patients Undergoing Vestibular Schwannoma Resection
Sinha, S., Williams, S. C., HANRAHAN, J. G., Muirhead, W. R., Booker, J., Khalil, S. S., Kitchen, N. D., Newall, N., Obholzer, R., Saeed, S. R., et al. (2024) · World Neurosurgery · doi:10.1016/j.wneu.2024.07.157
Ramachandran, R. A., Köseoğlu, M., Cinka, E. I., Barão, V. A. R., Bayındır, F., Wee, A. G., Yuan, J. C., & Sukotjo, C. (2025) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2024.12.012
Semi-Supervised PARAFAC2 Decomposition for Computational Phenotyping Using Electronic Health Records
Konyar, E., & Gahrooei, M. R. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3530271 · Cited by 1
Hierarchical Owen Value Explanations for Interpretable Brain Tumor Classification in Medical ImagingPreprint
Li, S., Chen, J., & Meng, F. (2025) · Research Square · doi:10.21203/rs.3.rs-7117910/v1
AI in Diagnostics and Treatment Planning
Panigrahy, U. P., Verma, A., SHARMA, A. K., Sharma, S., & Sagapola, R. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898811563125010009
Multimorbidity measurement strategies for predicting hospital visits
Neves, B. D., Moreira, J. M., Gonçalves, S., Cerejo, J., Mota, I., Silva, N. A., Leite, F., & Silva, M. J. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-13817-z
Fridgeirsson, E. A., Sontag, D. A., & Rijnbeek, P. R. (2023) · Figshare · doi:10.6084/m9.figshare.24770267
Healthcare Informatics: Transforming Patient Care with Multimodal Machine Learning
Achan, P., Nair, N., & Narayanan, S. (2025) · Springer handbooks · doi:10.1007/978-3-031-81728-1_40
Deep Learning Approach Based on PSOBPNN Classification Using IoT Healthcare Environment
Balakumar, C., & Venkatesh, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-8104-4_52
Transforming Healthcare: Deep Learning Techniques and Their Applications
Gulati, V., Gupta, P., Kaur, P., Gupta, K., & Rana, A. K. (2025) · International Conference on Computational Intelligence and Computing Applications (ICCICA) · doi:10.1109/iccica67008.2025.11337644
Bae, S., Kim, Y., Park, S., Kim, H., & Park, B. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1722338
Multimodal Fusion of Chest Radiographs and Text Reports for Heart Failure Detection
Rajaratnam, A., & Boukouvalas, Z. (2025) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata66926.2025.11401762
Machine learning for large observational datasets in healthcare
Morel, M. (2021) · theses.fr (ABES) · doi:10.70675/4c309e60zdba1z4d73za007zbd512e238110
Semantic analysis of SNOMED CT concept co-occurrences in clinical documentation using MIMIC-IV
Noori, A., & Manda, P. (2026) · Digital Health · doi:10.1177/20552076261436869 · Cited by 2
Huang, J.-L., Yang, C.-H., & Pai, T. (2026) · doi:10.2196/preprints.95929
Implementation of Generative AI in Biomedical Research and Healthcare
Nikolopoulos, A., & Karalis, V. D. (2026) · Applied Biosciences · doi:10.3390/applbiosci5020034
Priyadharshini, U., & Vijayan, R. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1844707
Bhargav, P., Kumar, Y., Changela, A., & Shrivastav, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45374-8.00022-8
Altememi, M. A., Favaloro, E. J., Islam, M. Z., Ng, S., Hsu, D. C., & Santhakumar, A. B. (2026) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.70217
Begum, F., & Uddin, Y. M. S. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.17946
Cho, H. N., Sutari, S., Lopez, A., Bow, H. C., & Zheng, K. (2026) · Research Square · doi:10.21203/rs.3.rs-10624154/v1
Nakamura, H., & Kato, Y. (2022) · Journal of Health Informatics and Digital Systems · doi:10.68159/n552358828
Artificial intelligence and target trial emulation: toward scalable and credible real-world evidence
Liu, T., & Wang, Y. (2026) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2026.112501
Bennett, O., Collins, H., Foster, J., & Wright, E. (2026) · Journal of Medical Sciences and Interdisciplinary Research · doi:10.51847/hu5vosiang
Li, C., Yu, W., & Zhang, W. (2026) · Journal of Health Informatics and Digital Systems · doi:10.68159/k304401829
Hassan, A., Siddiqui, N., Khan, B., & Malik, S. (2023) · Journal of Health Informatics and Digital Systems · doi:10.68159/a329131137
O’Connor, P., & Murphy, S. (2025) · Journal of Health Informatics and Digital Systems · doi:10.68159/f395662833
A data-driven and interpretable hybrid quantum-classical model for male infertility risk assessment
Sarath, T., & Brindha, K. (2026) · Frontiers in Medical Technology · doi:10.3389/fmedt.2026.1853728
Huang, J.-L., Yang, C.-H., & Pai, T. (2026) · JMIR Medical Informatics · doi:10.2196/95929
Yan, Y., Li, G., Chen, T., Yan, J., Wang, C., Yuan, A., Zhang, J., & Pu, J. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1962796
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 ›
Are you an author, or do you see a problem with this page? Request a review
