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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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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
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471
Speech recognition with deep recurrent neural networks
Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
Practical Bayesian Optimization of Machine Learning Algorithms
Snoek, J., Larochelle, H., & Adams, R. P. (2012) · arXiv · doi:10.48550/arxiv.1206.2944 · Cited by 6,691
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309
Deep learning in medical image analysis
Shen, D., Wu, G., & Suk, H.-I. (2017) · Annual Review of Biomedical Engineering · doi:10.1146/annurev-bioeng-071516-044442 · Cited by 4,968
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Universal Language Model Fine-tuning for Text Classification
Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100
Cyclical Learning Rates for Training Neural Networks
Smith, L. N. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.58 · Cited by 2,931
Data Resource Profile: Clinical Practice Research Datalink (CPRD)
Herrett, E., Gallagher, A. M., Bhaskaran, K., Forbes, H., Mathur, R., van Staa, T., & Smeeth, L. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv098 · Cited by 2,667 · cited 3×
“Given the data on demographics, diagnoses, therapies, and tests together with its linkage to other health-related databases, the CPRD is a valuable source of healthcare data [57].”
BackgroundInfluentialRokach, L. (2010) · Artificial Intelligence Review · doi:10.1007/s10462-009-9124-7 · Cited by 2,657
Scalable and accurate deep learning with electronic health records
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648
Proceedings of the 23rd international conference on Machine learning
Cohen, W. W., & Moore, A. (2006) · Cited by 2,647
Scalable and accurate deep learning with electronic health records.
Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., et al. (2018) · eScholarship (California Digital Library) · Cited by 2,242
Deep Learning for Health Informatics
Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., & Yang, G.-Z. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2636665 · Cited by 2,000
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningPreprint
Lemaître, G., Nogueira, F., & Aridas, C. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.06570 · Cited by 1,923
Regression modelling strategies for improved prognostic prediction
Harrell, F. E., Lee, K. L., Califf, R. M., Pryor, D. B., & Rosati, R. A. (1984) · Statistics in Medicine · doi:10.1002/sim.4780030207 · Cited by 1,804
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712
Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710
Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688
Mining electronic health records: towards better research applications and clinical care
Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3208 · Cited by 1,680
What's In A Name? Malay Seals As Onomastic Sources
Gallop, A. T. (2018) · Malay Literature · doi:10.37052/ml.31(1)no1 · Cited by 1,495
Deep learning for computational biology
Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473
Conrad, N., Judge, A., Tran, J., Mohseni, H., Hedgecott, D., Perez-Crespillo, A., Allison, M., Hemingway, H., Cleland, J. G. F., McMurray, J. J. V., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)32520-5 · Cited by 1,390
Self-citationRecurrent neural networks and robust time series prediction
Connor, J. T., Martin, R. D., & Atlas, L. E. (1994) · IEEE Transactions on Neural Networks · doi:10.1109/72.279188 · Cited by 1,316
Precision Medicine — Personalized, Problematic, and Promising
Jameson, J. L., & Longo, D. L. (2015) · New England Journal of Medicine · doi:10.1056/nejmsb1503104 · Cited by 1,149
Medical Records That Guide and Teach
Weed, L. L. (1968) · New England Journal of Medicine · doi:10.1056/nejm196803142781105 · Cited by 1,003
Using recurrent neural network models for early detection of heart failure onset
Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw112 · Cited by 928
Savova, G. K., Kipper-Schuler, K. C., Hurdle, J. F., & Meystre, S. M. (2008) · Yearbook of Medical Informatics · doi:10.1055/s-0038-1638592 · Cited by 895
Medication-related clinical decision support in computerized provider order entry systems: a review
Kuperman, G. J., Bobb, A., Payne, T. H., Avery, A. J., Gandhi, T. K., Burns, G., Classen, D. C., & Bates, D. W. (2007) · Journal of the American Medical Informatics Association · doi:10.1197/jamia.m2170 · Cited by 763
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723
GRAM: Graph-based Attention Model for Healthcare Representation Learning
Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2017) · Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/3097983.3098126 · Cited by 637
Identification of type 2 diabetes subgroups through topological analysis of patient similarity
Li, L., Cheng, W., Glicksberg, B. S., Gottesman, O., Tamler, R., Chen, R., Böttinger, E. P., & Dudley, J. T. (2015) · Science Translational Medicine · doi:10.1126/scitranslmed.aaa9364 · Cited by 554
Detecting Cancer Metastases on Gigapixel Pathology Images
Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506
Interpretability of deep learning models: A survey of results
Chakraborty, S., Tomsett, R., Raghavendra, R., Harborne, D., Alzantot, M., Cerutti, F., Srivastava, M. B., Preece, A. D., Julier, S., Rao, R., et al. (2017) · IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI) · doi:10.1109/uic-atc.2017.8397411 · Cited by 502
Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals
Boyd, K., Eng, K. H., & Page, C. D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40994-3_29 · Cited by 470
Multi-layer Representation Learning for Medical Concepts
Choi, E., Bahadori, M. T., Searles, E., Coffey, C., Thompson, M., Bost, J., Tejedor-Sojo, J., & Sun, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939823 · Cited by 469
Medical Records That Guide and Teach
Weed, L. L. (1968) · New England Journal of Medicine · doi:10.1056/nejm196803212781204 · Cited by 454
Predicting healthcare trajectories from medical records: a deep learning approach
Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.001 · Cited by 447
Proceedings of the 2017 SIAM International Conference on Data Mining
(2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973 · Cited by 434
$\mathtt {Deepr}$: A Convolutional Net for Medical Records
Nguyen, P., Tran, T., Wickramasinghe, N. S., & Venkatesh, S. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2633963 · Cited by 432
Comorbidity clusters in autism spectrum disorders: an electronic health record time-series analysis
Doshi-Velez, F., Ge, Y., & Kohane, I. (2014) · Pediatrics · doi:10.1542/peds.2013-0819 · Cited by 412
Ömürlü, İ. K., Türe, M., & Kurum, A. T. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.09.004 · Cited by 406
A comparison of word embeddings for the biomedical natural language processing
Wang, Y., Liu, S., Afzal, N., Rastegar-Mojarad, M., Wang, L., Shen, F., Kingsbury, P., & Liu, H. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.09.008 · Cited by 370
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
(2018) · doi:10.1109/wacv41190.2018 · Cited by 350
Uses of Electronic Health Records for Public Health Surveillance to Advance Public Health
Birkhead, G. S., Klompas, M., & Shah, N. R. (2015) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-031914-122747 · Cited by 345
Austin, P. C., Tu, J. V., Ho, J. E., Levy, D. A., & Lee, D. S. (2013) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.11.008 · Cited by 340
Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.
(2010) · PubMed Central · Cited by 332
Jiang, M., Chen, Y., Liu, M., Rosenbloom, S. T., Mani, S., Denny, J. C., & Xu, H. (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000163 · Cited by 292
Learning representations for the early detection of sepsis with deep neural networks
Kam, H. J., & Kim, H. Y. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.015 · Cited by 218
Tran, T., Nguyen, T. D., Phung, D., & Venkatesh, S. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.01.012 · Cited by 172
Emdin, C. A., Anderson, S. G., Callender, T., Conrad, N., Salimi‐Khorshidi, G., Mohseni, H., Woodward, M., & Rahimi, K. (2015) · BMJ · doi:10.1136/bmj.h4865 · Cited by 166
Self-citationPredicting hospital readmission for lupus patients: An RNN-LSTM-based deep-learning methodology
Reddy, B. K., & Delen, D. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.08.029 · Cited by 163
GRAM: Graph-based Attention Model for Healthcare Representation LearningPreprint
Choi, E., Bahadori, M. T., Song, L., Stewart, W. F., & Sun, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.07012 · Cited by 163
Erratum: Area under the Precision-Recall Curve: Point Estimates and Confidence Intervals
Boyd, K., Eng, K. H., & Page, C. D. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40994-3_55 · Cited by 157
Rahimian, F., Salimi‐Khorshidi, G., Payberah, A. H., Tran, J., Solares, R. A., Raimondi, F. E. D., Nazarzadeh, M., Canoy, D., & Rahimi, K. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002695 · Cited by 147
Self-citationZhao, D., & Weng, C.-H. (2011) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2011.05.004 · Cited by 139
Hippisley-Cox, J., & Coupland, C. (2013) · BMJ Open · doi:10.1136/bmjopen-2013-003482 · Cited by 123
Blood Pressure and Risk of Vascular Dementia
Emdin, C. A., Rothwell, P. M., Salimi‐Khorshidi, G., Kiran, A., Conrad, N., Callender, T., Mehta, Z., Pendlebury, S. T., Anderson, S. G., Mohseni, H., et al. (2016) · Stroke · doi:10.1161/strokeaha.116.012658 · Cited by 113
Self-citationMiotto, R., & Weng, C. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu050 · Cited by 104
Bayesian Layers: A Module for Neural Network UncertaintyPreprint
Tran, D., Dusenberry, M. W., van der Wilk, M., & Hafner, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.03973 · Cited by 94
Ma, T., Xiao, C., & Wang, F. (2018) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611975321.30 · Cited by 81
Jacobs, L., Efremov, L., Ferreira, J. P., Thijs, L., Yang, W., Zhang, Z., Latini, R., Masson, S., Agabiti, N., Sever, P. S., et al. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.116.005231 · Cited by 53
Rahimi, K., Mohseni, H., Otto, C. M., Conrad, N., Tran, J., Nazarzadeh, M., Woodward, M., Dwyer, T., & MacMahon, S. W. (2017) · PLoS Medicine · doi:10.1371/journal.pmed.1002404 · Cited by 51
Self-citationThreshold-Free Measures for Assessing the Performance of Medical Screening Tests
Yuan, Y., Su, W., & Zhu, M. (2015) · Frontiers in Public Health · doi:10.3389/fpubh.2015.00057 · Cited by 31
Disease-Atlas: Navigating Disease Trajectories with Deep LearningPreprint
Lim, B., & van der Schaar, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.10254 · Cited by 12
Evolving Deep Neural NetworksPreprint
Miikkulainen, R. P., Liang, J., Meyerson, E., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00548 · Cited by 7
Effective Building Block Design for Deep Convolutional Neural Networks using SearchPreprint
Dutta, J., Liu, J., Kurup, U., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.08577 · Cited by 3
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
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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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