Deep patient: An unsupervised representation to predict the future of patients from the electronic health records
Open accessAbstract
Secondary use of electronic health records (EHRs) promises to advance clinical research and better inform clinical decision making. Challenges in summarizing and representing patient data prevent widespread practice of predictive modeling using EHRs. Here we present a novel unsupervised deep feature learning method to derive a general-purpose patient representation from EHR data that facilitates clinical predictive modeling. In particular, a three-layer stack of denoising autoencoders was used to capture hierarchical regularities and dependencies in the aggregated EHRs of about 700,000 patients from the Mount Sinai data warehouse. The result is a representation we name "deep patient". We evaluated this representation as broadly predictive of health states by assessing the probability of patients to develop various diseases. We performed evaluation using 76,214 test patients comprising 78 diseases from diverse clinical domains and temporal windows. Our results significantly outperformed those achieved using representations based on raw EHR data and alternative feature learning strategies. Prediction performance for severe diabetes, schizophrenia, and various cancers were among the top performing. These findings indicate that deep learning applied to EHRs can derive patient representations that offer improved clinical predictions, and could provide a machine learning framework for augmenting clinical decision systems.
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Many existing XAI tools in Radiation Oncology rely on single-modality data or exclude unstructured data sources such as clinical narratives and imaging.
“Many existing XAI tools in Radiation Oncology rely on single-modality data24 or exclude unstructured data sources such as clinical narratives and imaging.25,26”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [25]
The citation is attached to the claim that tools exclude unstructured sources such as clinical narratives and imaging. In Material and Methods > EHR Processing (page unavailable), this paper says it retained free-text clinical notes and extracted concepts from them—the opposite of excluding clinical narratives. The cited passages do not establish a claim about XAI tools in radiation oncology. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredMiotto et al. applied a stacked DAE to multimodal electronic health records data, effectively representing patients in a lower dimensional space and thereby enabling a multitude of clinical predictive modeling such as the onset of disease.
“Miotto et al. [37] applied a stacked DAE to multimodal electronic health records (EHR) data, effectively representing patients in a lower dimensional space and thereby enabling a multitude of clinical predictive modeling such as the onset of disease.”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [37]
Among 37 studies learning from structured codes, the frequency of a given code was used in 5 studies.
“Among 37 studies learning from structured codes, the majority (n = 27) generate one-hot vectors for the input data, and the frequency of a given code is used in 5 studies [11,48,58–60].”
From Deep representation learning of patient data from Electronic Health Records (EHR): A systematic review (2021) · cites this paper as [11]
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Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337 · see citing works
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278 · see citing works
Introduction to Information Retrieval
Manning, C. D., Raghavan, P., & Schütze, H. (2008) · doi:10.1017/cbo9780511809071 · Cited by 11,288 · see citing works
Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955 · see citing works
Blei, D. M. (2012) · Communications of the ACM · doi:10.1145/2133806.2133826 · Cited by 5,516 · see citing works
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works
Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning
Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873 · see citing works
Graph-based approach for airborne light detection and ranging segmentation
Vilariño, D. L., Cabaleiro, J. C., Sánchez, J. M., Rivera, F. F., & Pena, T. F. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.015020 · Cited by 1,760 · see citing works
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 · see citing works
The human splicing code reveals new insights into the genetic determinants of disease
Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K. C., Hua, Y., Gueroussov, S., Najafabadi, H. S., Hughes, T., et al. (2014) · Science · doi:10.1126/science.1254806 · Cited by 1,225 · see citing works
Weiskopf, N. G., & Weng, C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000681 · Cited by 1,216 · cited 1× · see citing works
“EHR data is challenging to represent and model due to its high dimensionality, noise, heterogeneity, sparseness, incompleteness, random errors, and systematic biases 7,10,11 .”
BackgroundDeep Neural Nets as a Method for Quantitative Structure–Activity Relationships
Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500747n · Cited by 1,159 · see citing works
A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries
Chapman, W. W., Bridewell, W. B., Hanbury, P., Cooper, G. F., & Buchanan, B. G. (2001) · Journal of Biomedical Informatics · doi:10.1006/jbin.2001.1029 · Cited by 1,121 · see citing works
Exploring Strategies for Training Deep Neural Networks
Larochelle, H., Bengio, Y., Louradour, J., & Lamblin, P. (2009) · doi:10.1145/1577069.1577070 · Cited by 1,109 · see citing works
Connectomic reconstruction of the inner plexiform layer in the mouse retina
Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013) · Nature · doi:10.1038/nature12346 · Cited by 997 · see citing works
Data-driven prediction of drug effects and interactions
Tatonetti, N. P., Ye, P. P., Daneshjou, R., & Altman, R. B. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003377 · Cited by 936 · see citing works
Predictive data mining in clinical medicine: Current issues and guidelines
BELLAZZI, R., & ZUPAN, B. (2008) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2006.11.006 · Cited by 823 · see citing works
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 548 · see citing works
Self-citationDeep learning of the tissue-regulated splicing code
Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu277 · Cited by 459 · see citing works
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 · see citing works
Prediction Modeling Using EHR Data
Wu, J., Roy, J. A., & Stewart, W. F. (2010) · Medical Care · doi:10.1097/mlr.0b013e3181de9e17 · Cited by 374 · see citing works
Defining and measuring completeness of electronic health records for secondary use
Weiskopf, N. G., Hripcsak, G., Swaminathan, S., & Weng, C. (2013) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2013.06.010 · Cited by 367 · see citing works
Lasko, T. A., Denny, J. C., & Levy, M. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066341 · Cited by 313 · see citing works
The National Center for Biomedical Ontology
Musen, M. A., Noy, N. F., Shah, N. H., Whetzel, P. L., Chute, C. G., Story, M.-A., Smith, B., & and the NCBO team (2011) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000523 · Cited by 278 · see citing works
The open biomedical annotator.
Jonquet, C., Shah, N. H., & Musen, M. A. (2009) · PubMed · Cited by 253 · see citing works
Diagnosis code assignment: models and evaluation metrics
Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., & Elhadad, N. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002159 · Cited by 244 · see citing works
Unsupervised learning of disease progression models
Wang, X., Sontag, D. A., & Wang, F. (2014) · ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2623330.2623754 · Cited by 220 · see citing works
Deep learning for healthcare decision making with EMRs
Liang, Z., Zhang, G., Huang, J. X., & Hu, Q. V. (2014) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2014.6999219 · Cited by 187 · see citing works
Comparison of concept recognizers for building the Open Biomedical Annotator
Shah, N. H., Bhatia, N., Jonquet, C., Rubin, D. L., Chiang, A. P.-F., & Musen, M. A. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-s9-s14 · Cited by 130 · see citing works
Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocv024 · Cited by 128 · see citing works
Cohen, R., Elhadad, M., & Elhadad, N. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-10 · Cited by 121 · see citing works
Cowen, M. E., Dusseau, D. J., Toth, B. G., Guisinger, C., Zodet, M. W., & Shyr, Y. (1998) · Medical Care · doi:10.1097/00005650-199807000-00016 · Cited by 110 · see citing works
Hierarchically Supervised Latent Dirichlet Allocation
Perotte, R., Wood, F., Elhadad, N., & Bartlett, N. (2011) · Cited by 109 · see citing works
Hersh, W. (2007) · PubMed · Cited by 108 · see citing works
Toward personalizing treatment for depression: predicting diagnosis and severity
Huang, S. H., LePendu, P., Iyer, S. V., Tai‐Seale, M., Carrell, D., & Shah, N. H. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-002733 · Cited by 108 · see citing works
Mining FDA drug labels using an unsupervised learning technique - topic modeling
Bişğin, H., Liu, Z., Fang, H., Xu, X., & Tong, W. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-s10-s11 · Cited by 107 · see citing works
Miotto, R., & Weng, C. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu050 · Cited by 97 · see citing works
Self-citationAnnotation Analysis for Testing Drug Safety Signals using Unstructured Clinical Notes
LePendu, P., Iyer, S. V., Fairon, C., & Shah, N. H. (2012) · Journal of Biomedical Semantics · doi:10.1186/2041-1480-3-s1-s5 · Cited by 95 · see citing works
A method for inferring medical diagnoses from patient similarities
Gottlieb, A., Stein, G. Y., Ruppin, E., Altman, R. B., & Sharan, R. (2013) · BMC Medicine · doi:10.1186/1741-7015-11-194 · Cited by 89 · see citing works
Clinical Case-based Retrieval Using Latent Topic Analysis.
Arnold, C. W., El-Saden, S. M., Bui, A. A. T., & Taira, R. K. (2010) · PubMed Central · Cited by 63 · see citing works
Kennedy, E. H., Wiitala, W. L., Hayward, R. A., & Sussman, J. B. (2012) · Medical Care · doi:10.1097/mlr.0b013e31827da594 · Cited by 63 · see citing works
Identifying phenotypic signatures of neuropsychiatric disorders from electronic medical records
Lyalina, S., Percha, B., LePendu, P., Iyer, S. V., Altman, R. B., & Shah, N. H. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001933 · Cited by 61 · see citing works
Electronic health records: Implications for drug discovery
Yao, L., Zhang, Y., Li, Y., Sanséau, P., & Agarwal, P. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.05.009 · Cited by 50 · see citing works
Predictability Bounds of Electronic Health Records
Dahlem, D., Maniloff, D., & Ratti, C. (2015) · Scientific Reports · doi:10.1038/srep11865 · Cited by 26 · see citing works
Prediction and Informative Risk Factor Selection of Bone Diseases
Li, H., Xiao-yi, L., Ramanathan, M. K., & Zhang, A. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2330579 · Cited by 23 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688 · see citing works
A guide to deep learning in healthcare
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0316-z · Cited by 5,312 · see citing works
Deep learning for healthcare: review, opportunities and challenges
Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works
Self-citationScalable 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 · see citing works
“In a wellknown work, investigators used auto-encoders to predict a specific set of diagnoses.(29) Subsequent work extended this approach by modeling the temporal sequence of events that occurred in a patient’s record, which may enhance accuracy in scenarios that depend on the order of events, with convolutional and re…”
MethodThe “All of Us” Research Program
The All of Us Research Program Investigators (2019) · New England Journal of Medicine · doi:10.1056/nejmsr1809937 · Cited by 2,440 · see citing works
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks
Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. M. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.01.010 · Cited by 1,561 · see citing works
Artificial intelligence in cardiology
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., Ashley, E., & Dudley, J. T. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.03.521 · Cited by 1,202 · see citing works
Self-citationA clinically applicable approach to continuous prediction of future acute kidney injury
Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Nature · doi:10.1038/s41586-019-1390-1 · Cited by 1,086 · see citing works
Deep learning-based multi-omics integration robustly predicts survival in liver cancer
Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0853 · Cited by 1,091 · see citing works
Machine Learning Approaches for Clinical Psychology and Psychiatry
Dwyer, D. B., Falkai, P., & Koutsouleris, N. (2018) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-045037 · Cited by 966 · see citing works
Artificial intelligence for mental health and mental illnesses: an overview
Graham, S., Depp, C., Lee, E. E., Nebeker, C., Tu, X., Kim, H.-C., & Jeste, D. V. (2019) · Current Psychiatry Reports · doi:10.1007/s11920-019-1094-0 · Cited by 970 · see citing works
The ‘Digital Twin’ to enable the vision of precision cardiology
Acero, J. C., Margara, F., Marciniak, M., Rodero, C., Lončarić, F., Feng, Y., Gilbert, A., Fernandes, J. F., Bukhari, S. H. A., Wajdan, A., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa159 · Cited by 987 · see citing works
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802 · see citing works
Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834 · see citing works
The “All of Us” Research Program
Sullivan, F., McKinstry, B., & Vasishta, S. (2019) · New England Journal of Medicine · doi:10.1056/nejmc1912496 · Cited by 855 · see citing works
Current Applications and Future Impact of Machine Learning in Radiology
Choy, G., Khalilzadeh, O., Michalski, M., Do, S., Samir, A. E., Pianykh, O. S., Geis, J. R., Pandharipande, P. V., Brink, J. A., & Dreyer, K. J. (2018) · Radiology · doi:10.1148/radiol.2018171820 · Cited by 800 · see citing works
Xiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · see citing works
Wang, Y., Zhang, S., Li, F., Zhou, Y., Zhang, Y., Wang, Z., Zhang, R., Zhu, J., Ren, Y., Tan, Y. C., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz981 · Cited by 794 · see citing works
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2015) · Europe PMC (PubMed Central) · doi:10.48550/arxiv.1511.05942 · Cited by 793 · see citing works
Transparency and the black box problem: why we do not trust AI
von Eschenbach, W. J. (2021) · Philosophy & Technology · doi:10.1007/s13347-021-00477-0 · Cited by 786 · see citing works
Multimodal deep learning for biomedical data fusion: a review
Stahlschmidt, S. R., Ulfenborg, B., & Synnergren, J. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbab569 · Cited by 766 · see citing works
“[37] applied a stacked DAE to multimodal electronic health records (EHR) data, effectively representing patients in a lower dimensional space and thereby enabling a multitude of clinical predictive modeling such as the onset of disease.”
MethodMultimodal deep learning models for early detection of Alzheimer’s disease stage
Venugopalan, J., Tong, L., Hassanzadeh, H. R., & Wang, M. D. (2021) · Scientific Reports · doi:10.1038/s41598-020-74399-w · Cited by 732 · see citing works
Artificial Intelligence in Cardiovascular Imaging
Dey, D., Slomka, P. J., Leeson, P., Comaniciu, D., Shrestha, S., Sengupta, P. P., & Marwick, T. H. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.12.054 · Cited by 685 · see citing works
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 · see citing works
Introducing artificial intelligence training in medical education
Paranjape, K., Schinkel, M., Nannan Panday, R., Car, J., & Nanayakkara, P. (2019) · JMIR Medical Education · doi:10.2196/16048 · Cited by 634 · see citing works
Vellido, A. (2020) · Neural Computing and Applications · doi:10.1007/s00521-019-04051-w · Cited by 635 · see citing works
BEHRT: Transformer for Electronic Health Records
Li, Y., Rao, S., Solares, J. R. A., Hassaine, A., Ramakrishnan, R., Canoy, D., Zhu, Y., Rahimi, K., & Salimi-Khorshidi, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-62922-y · Cited by 595 · see citing works
(2016) · PubMed · Cited by 576 · see citing works
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review
Sheikhalishahi, S., Miotto, R., Dudley, J. T., Lavelli, A., Rinaldi, F., & Osmani, V. (2019) · JMIR Medical Informatics · doi:10.2196/12239 · Cited by 558 · see citing works
Self-citationSex and gender differences and biases in artificial intelligence for biomedicine and healthcare
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M. J., Chadha, A. S., et al. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0288-5 · Cited by 553 · see citing works
Artificial Intelligence and Personalized Medicine
Schork, N. J. (2019) · Cancer treatment and research · doi:10.1007/978-3-030-16391-4_11 · Cited by 518 · see citing works
Security and privacy of electronic health records: Concerns and challenges
Keshta, I., & Odeh, A. (2021) · Egyptian Informatics Journal · doi:10.1016/j.eij.2020.07.003 · Cited by 498 · see citing works
Leveraging uncertainty information from deep neural networks for disease detection
Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S. (2017) · Scientific Reports · doi:10.1038/s41598-017-17876-z · Cited by 510 · see citing works
Artificial intelligence in clinical and genomic diagnostics
Dias, R., & Torkamani, A. (2019) · Genome Medicine · doi:10.1186/s13073-019-0689-8 · Cited by 474 · see citing works
RETRACTED ARTICLE: Artificial intelligence and machine learning in precision and genomic medicine
Quazi, S. (2022) · Medical Oncology · doi:10.1007/s12032-022-01711-1 · Cited by 455 · see citing works
RetractedOn Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks
Thulasidasan, S., (NNSA), U. N. N. S. A., Chennupati, G., Bilmes, J., Tanmoy Bhattacharya, A., & Michalak, S. (2019) · Neural Information Processing Systems · doi:10.2172/1525811 · Cited by 423 · see citing works
From hype to reality: data science enabling personalized medicine
Fröhlich, H., Balling, R., Beerenwinkel, N., Kohlbacher, O., Kumar, S., Lengauer, T., Maathuis, M. H., Moreau, Y., Murphy, S. A., Przytycka, T. M., et al. (2018) · BMC Medicine · doi:10.1186/s12916-018-1122-7 · Cited by 413 · see citing works
The Application of Deep Learning in Cancer Prognosis Prediction
Zhu, W., Xie, L., Han, J., & Guo, X. (2020) · Cancers · doi:10.3390/cancers12030603 · Cited by 384 · see citing works
$\mathtt {Deepr}$: 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 413 · see citing works
Early detection of type 2 diabetes mellitus using machine learning-based prediction models
Kopitar, L., Kocbek, P., Cilar, L., Sheikh, A., & Stiglic, G. (2020) · Scientific Reports · doi:10.1038/s41598-020-68771-z · Cited by 369 · see citing works
Distributed deep learning networks among institutions for medical imaging
Chang, K., Balachandar, N., Lam, C., Yi, D., Brown, J., Beers, A., Rosen, B., Rubin, D. L., & Kalpathy-Cramer, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy017 · Cited by 340 · see citing works
Zhang, B., Shi, H., & Wang, H. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s410301 · Cited by 347 · see citing works
Machine Learning and the Future of Cardiovascular Care
Quer, G., Arnaout, R., Henne, M., & Arnaout, R. (2021) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2020.11.030 · Cited by 346 · see citing works
A review in radiomics: Making personalized medicine a reality via routine imaging
Guiot, J., Vaidyanathan, A., Deprez, L., Zerka, F., Danthine, D., Frix, A.-N., Lambin, P., Bottari, F., Tsoutzidis, N., Miraglio, B., et al. (2021) · Medicinal Research Reviews · doi:10.1002/med.21846 · Cited by 344 · see citing works
Network biology concepts in complex disease comorbidities
Hu, J. X., Thomas, C. E., & Brunak, S. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.87 · Cited by 328 · see citing works
A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues
Shamshirband, S., Fathi, M., Dehzangi, A., Chronopoulos, A. T., & Alinejad‐Rokny, H. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103627 · Cited by 323 · see citing works
Xu, J., Yang, P., Xue, S., Sharma, B., Sanchez-Martin, M., Wang, F., Beaty, K. A., Dehan, E., & Parikh, B. (2019) · Human Genetics · doi:10.1007/s00439-019-01970-5 · Cited by 305 · see citing works
Survey on deep learning for radiotherapy
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.05.018 · Cited by 302 · see citing works
Mining Electronic Health Records (EHRs): A Survey
Yadav, P., Steinbach, M., Kumar, V., & Simon, G. (2018) · ACM Computing Surveys · doi:10.1145/3127881 · Cited by 299 · see citing works
Tensor Graph Convolutional Networks for Text Classification
Liu, X., You, X., Zhang, X., Wu, J. Y., & Lv, P. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6359 · Cited by 299 · see citing works
Machine Learning for Health Services Researchers
Doupé, P., Faghmous, J. H., & Basu, S. (2019) · Value in Health · doi:10.1016/j.jval.2019.02.012 · Cited by 298 · see citing works
Machine learning in clinical decision making
Adlung, L., Cohen, Y., Mor, U., & Elinav, E. (2021) · Med · doi:10.1016/j.medj.2021.04.006 · Cited by 304 · see citing works
Big Data and Data Science in Critical Care
Sanchez-Pinto, L. N., Luo, Y., & Churpek, M. M. (2018) · CHEST Journal · doi:10.1016/j.chest.2018.04.037 · Cited by 284 · see citing works
Prediction of gestational diabetes based on nationwide electronic health records
Artzi, N. S., Shilo, S., Hadar, E., Rossman, H., Barbash‐Hazan, S., Ben‐Haroush, A., Balicer, R. D., Feldman, B., Wiznitzer, A., & Segal, E. (2020) · Nature Medicine · doi:10.1038/s41591-019-0724-8 · Cited by 289 · see citing works
Deep neural networks in psychiatry
Durstewitz, D., Koppe, G., & Meyer‐Lindenberg, A. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0365-9 · Cited by 287 · see citing works
Recommendations for Reporting Machine Learning Analyses in Clinical Research
Stevens, L. M., Mortazavi, B. J., Deo, R. C., Curtis, L., & Kao, D. P. (2020) · Circulation: Cardiovascular Quality and Outcomes · doi:10.1161/circoutcomes.120.006556 · Cited by 268 · see citing works
Chakraborty, C., Bhattacharya, M., Pal, S., & Lee, S. (2023) · Current Research in Biotechnology · doi:10.1016/j.crbiot.2023.100164 · Cited by 295 · see citing works
Deep Learning for Diabetes: A Systematic Review
Zhu, T., Li, K., Herrero, P., & Georgiou, P. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3040225 · Cited by 275 · see citing works
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Choi, E., Xu, Z., Li, Y., Dusenberry, M. W., Flores, G., Xue, E., & Dai, A. M. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5400 · Cited by 277 · see citing works
Deep learning and the electrocardiogram: review of the current state-of-the-art
Somani, S. S., Russak, A. J., Richter, F., Zhao, S., Vaid, A., Chaudhry, F. F., Freitas, J. K. D., Naik, N., Miotto, R., Nadkarni, G. N., et al. (2020) · EP Europace · doi:10.1093/europace/euaa377 · Cited by 267 · see citing works
Self-citationKhosravi, M., Zare, Z., Mojtabaeian, S. M., & Izadi, R. (2024) · Health Services Research and Managerial Epidemiology · doi:10.1177/23333928241234863 · Cited by 265 · see citing works
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification
Ding, K., Wang, J., Li, J., Li, D., & Liu, H. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.399 · Cited by 271 · see citing works
Neural Natural Language Processing for unstructured data in electronic health records: A review
Li, I., Pan, J. N., Goldwasser, J., Verma, N., Wong, W. P., Nuzumlalı, M. Y., Rosand, B., Li, Y., Zhang, M. M., Chang, D., et al. (2022) · Computer Science Review · doi:10.1016/j.cosrev.2022.100511 · Cited by 262 · see citing works
Artificial intelligence in positive mental health: a narrative review
Thakkar, A., Gupta, A., & Sousa, A. D. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1280235 · Cited by 299 · see citing works
Machine Learning in Radiology: Applications Beyond Image Interpretation
Lakhani, P. C., Prater, A. B., Hutson, R. K., Andriole, K. P., Dreyer, K. J., Morey, J., Prevedello, L. M. S., Clark, T. J., Geis, J. R., Itri, J. N., et al. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.09.044 · Cited by 255 · see citing works
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., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247 · see citing works
Velupillai, S. U., Suominen, H., Liakata, M., Roberts, A., Shah, A. D., Morley, K. I., Osborn, D., Hayes, J., Stewart, R. J., Downs, J. M., et al. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.10.005 · Cited by 253 · see citing works
Applications of Machine Learning in Real-Life Digital Health Interventions: Review of the Literature
Triantafyllidis, A. K., & Tsanas, A. (2019) · Journal of Medical Internet Research · doi:10.2196/12286 · Cited by 243 · see citing works
Machine Learning for Clinical Outcome Prediction
Shamout, F. E., Zhu, T.-T., & Clifton, D. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3007816 · Cited by 210 · see citing works
Steele, A. J., Cakiroglu, S. A., Shah, A. D., Denaxas, S., Hemingway, H., & Luscombe, N. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202344 · Cited by 241 · see citing works
Interpretable Deep Models for ICU Outcome Prediction.
Che, Z., Purushotham, S., Khemani, R. G., & Liu, Y. (2016) · PubMed Central · Cited by 240 · see citing works
Bisdas, S., Topriceanu, C.-C., Zakrzewska, Z., Irimia, A.-V., Shakallis, L., Subhash, J., Casapu, M.-M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., et al. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.795284 · Cited by 232 · see citing works
Si, Y., Du, J., Li, Z., Jiang, X., Miller, T., Wang, F., Jim Zheng, W., & Roberts, K. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103671 · Cited by 235 · see citing works
Machine learning model to predict mental health crises from electronic health records
Garriga, R., Mas, J., Abraha, S., Nolan, J., Harrison, O., Tadros, G., & Matic, A. (2022) · Nature Medicine · doi:10.1038/s41591-022-01811-5 · Cited by 229 · see citing works
Recent advances of deep learning in bioinformatics and computational biology
Tang, B., Pan, Z., Yin, K., & Khateeb, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00214 · Cited by 233 · see citing works
A scalable discrete-time survival model for neural networks
Gensheimer, M. F., & Narasimhan, B. (2019) · PeerJ · doi:10.7717/peerj.6257 · Cited by 232 · see citing works
An exposome perspective: Early-life events and immune development in a changing world
Renz, H., Holt, P. G., Inouye, M., Logan, A. C., Prescott, S. L., & Sly, P. D. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.05.015 · Cited by 226 · see citing works
Bibault, J., Giraud, P., Housset, M., Durdux, C., Taı̈eb, J., Berger, A., Coriat, R., Chaussade, S., Dousset, B. E., Nordlinger, B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30657-6 · Cited by 229 · see citing works
Understanding from Machine Learning Models
Sullivan, E. (2019) · The British Journal for the Philosophy of Science · doi:10.1093/bjps/axz035 · Cited by 229 · see citing works
Norgeot, B., Glicksberg, B. S., Trupin, L. S., Lituiev, D., Gianfrancesco, M. A., Oskotsky, B., Schmajuk, G., Yazdany, J., & Butte, A. J. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.0606 · Cited by 224 · see citing works
Machine learning for comprehensive forecasting of Alzheimer’s Disease progression
Fisher, C. K., Smith, A. M., Walsh, J. R., Diseases, C. A. M., Simon, A. J., Edgar, C., Jack, C. R., Holtzman, D., Russell, D., Hill, D., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-49656-2 · Cited by 223 · see citing works
Advances in Electronic Phenotyping: From Rule-Based Definitions to Machine Learning Models
Banda, J. M., Seneviratne, M. G., Hernandez‐Boussard, T., & Shah, N. H. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013315 · Cited by 223 · see citing works
Deep learning and alternative learning strategies for retrospective real-world clinical data
Chen, D. J., Liu, S., Kingsbury, P., Sohn, S., Storlie, C. B., Habermann, E. B., Naessens, J. M., Larson, D. W., & Liu, H. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0122-0 · Cited by 223 · see citing works
Kraljević, Ž., Searle, T., Shek, A., Roguski, Ł., Noor, K., Bean, D. M., Mascio, A., Zhu, L., Folarin, A., Roberts, A., et al. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102083 · Cited by 224 · see citing works
The role of artificial intelligence in medical imaging research
Tang, X. (2019) · BJR|Open · doi:10.1259/bjro.20190031 · Cited by 219 · see citing works
Graham, S. A., Lee, E., Jeste, D. V., Patten, R. V., Twamley, E. W., Nebeker, C., Yamada, Y., Kim, H., & Depp, C. A. (2019) · Psychiatry Research · doi:10.1016/j.psychres.2019.112732 · Cited by 223 · see citing works
Beyond Sparsity: Tree Regularization of Deep Models for Interpretability
Wu, M., Hughes, M., Parbhoo, S., Zazzi, M., Roth, V., & Doshi-Velez, F. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11501 · Cited by 213 · see citing works
Tebani, A., Afonso, C., Marret, S., & Bekri, S. (2016) · International Journal of Molecular Sciences · doi:10.3390/ijms17091555 · Cited by 214 · see citing works
Deep representation learning of electronic health records to unlock patient stratification at scale
Landi, I., Glicksberg, B. S., Lee, H.-C., Cherng, S., Landi, G., Danieletto, M., Dudley, J. T., Furlanello, C., & Miotto, R. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0301-z · Cited by 213 · see citing works
Self-citationMali, S. A., Ibrahim, A., Woodruff, H. C., Andrearczyk, V., Müller, H., Primakov, S. P., Salahuddin, Z., Chatterjee, A., & Lambin, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090842 · Cited by 218 · see citing works
Big data hurdles in precision medicine and precision public health
Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0719-2 · Cited by 200 · see citing works
Natural Language Processing for EHR-Based Computational Phenotyping
Zeng, Z., Deng, Y., Li, X., Naumann, T., & Luo, Y. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2849968 · Cited by 210 · see citing works
Structured prediction models for RNN based sequence labeling in clinical text
Jagannatha, A., & yu, H. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1082 · Cited by 215 · see citing works
Current Techniques for Diabetes Prediction: Review and Case Study
Larabi-Marie-Sainte, S., Aburahmah, L., Almohaini, R., & Saba, T. (2019) · Applied Sciences · doi:10.3390/app9214604 · Cited by 207 · see citing works
Readmission prediction using deep learning on electronic health records
Ashfaq, A., Sant’Anna, A., Lingman, M., & Nowaczyk, S. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103256 · Cited by 194 · see citing works
- Authors
- Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Dudley, Joel T.
- Venue
- Scientific Reports
- Published
- 2016-05-17
- Volume
- 6
- Issue
- 1
- Pages
- 26094-26094
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC4869115
- OpenAlex
- W2404901863
- Cited by
- 1,710
- References
- 49
- Citations by year
- 2026: 149 · 2025: 173 · 2024: 145 · 2023: 173 · 2022: 168 · 2021: 218 · 2020: 210 · 2019: 213 · 2018: 159 · 2017: 85 · 2016: 15 · 2015: 1
- FWCI
- 129.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001433)
- National Cancer Institute (U54CA189201)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK098242)
- National Center for Advancing Translational Sciences
- 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, denoising autoencoder, unsupervised feature learning, disease prediction, clinical decision support
- MeSH terms
- Machine Learning, Electronic Data Processing, Humans, Prognosis, Biostatistics, Electronic Health Records
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