Deep Learning to Predict Patient Future Diseases from the Electronic Health Records
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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 third world countries, PHS is mostly performed using manual procedures.
“As discussed above, PHS is a common practice world wide but mostly in third world countries it is performed using manual procedures [68], [69].”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [68]
Cites
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
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
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · 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
Deep 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
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
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
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
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
Annotation 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
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
Flagged first, then by citations
Cited by
Sorted by citations
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-citationXiao, C., Choi, E., & Sun, J. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy068 · Cited by 792 · see citing works
Xu, H., & Shuttleworth, K. M. J. (2024) · Intelligent Medicine · doi:10.1016/j.imed.2023.08.001 · Cited by 196 · see citing works
Healthcare 4.0: A review of frontiers in digital health
Jayaraman, P. P., Forkan, A. R. M., Morshed, A., Haghighi, P. D., & Kang, Y. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1350 · Cited by 191 · see citing works
Secondary Use of Electronic Health Record: Opportunities and Challenges
Shah, S. M., & Khan, R. A. (2020) · IEEE Access · doi:10.1109/access.2020.3011099 · Cited by 158 · see citing works
Zhang, L., Zhang, Z., Luo, Y., Cao, J., Xie, R., & Li, S. (2021) · Agricultural and Forest Meteorology · doi:10.1016/j.agrformet.2021.108666 · Cited by 152 · see citing works
Sekaran, K., Chandana, P., Krishna, N. M., & Kadry, S. (2019) · Multimedia Tools and Applications · doi:10.1007/s11042-019-7419-5 · Cited by 133 · see citing works
Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico
Baccouche, A., García-Zapirain, B., Castillo-Olea, C., & Elmaghraby, A. S. (2020) · Information · doi:10.3390/info11040207 · Cited by 130 · see citing works
Chen, G., Ding, C., Li, Y., Hu, X., Li, X., Ren, L., Ding, X., Tian, P., & Xue, W. (2020) · IEEE Access · doi:10.1109/access.2020.2995310 · Cited by 113 · see citing works
Improved Optimization Algorithm in LSTM to Predict Crop Yield
Bhimavarapu, U., Battineni, G., & Chintalapudi, N. (2023) · Computers · doi:10.3390/computers12010010 · Cited by 98 · see citing works
Deep Learning Application Pros And Cons Over Algorithm
Moshayedi, A. J., Roy, A. S., Kolahdooz, A., & Shuxin, Y. (2022) · EAI Endorsed Transactions on AI and Robotics · doi:10.4108/airo.v1i.19 · Cited by 92 · see citing works
Efficient Deep Learning Approaches for Health Informatics
Navamani, T. M. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816718-2.00014-2 · Cited by 60 · see citing works
Deep learning for health informatics: Recent trends and future directions
Srivastava, S., Soman, S., Rai, A., & Srivastava, P. K. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126082 · Cited by 52 · see citing works
Computer-aided diagnosis of hepatocellular carcinoma fusing imaging and structured health data
Menegotto, A. B., Becker, C. D. L., & Cazella, S. C. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00151-x · Cited by 45 · see citing works
Dargazany, A. R., Stegagno, P., & Mankodiya, K. (2018) · Mobile Information Systems · doi:10.1155/2018/8125126 · Cited by 45 · see citing works
Zhang, J., Gong, J., & Barnes, L. (2017) · 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2017.80 · Cited by 44 · see citing works
Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey
Sun, Y., & Zhang, D. (2019) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20190421122826 · Cited by 41 · see citing works
Miotto, R., Percha, B., Glicksberg, B. S., Lee, H.-C., Cruz, L. C., Dudley, J. T., & Nabeel, I. (2019) · JMIR Medical Informatics · doi:10.2196/16878 · Cited by 28 · see citing works
Self-citationYoon, H., Ramanathan, A., & Tourassi, G. D. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47898-2_21 · Cited by 29 · see citing works
Improving Winter Wheat Yield Forecasting Based on Multi-Source Data and Machine Learning
Sun, Y., Zhang, S., Tao, F., Aboelenein, R., & Amer, A. (2022) · Agriculture · doi:10.3390/agriculture12050571 · Cited by 27 · see citing works
Reith, F. H., Mormino, E. C., & Zaharchuk, G. (2021) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12212 · Cited by 20 · see citing works
Kashyap, A., Callison-Burch, C., & Boland, M. R. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104979 · Cited by 20 · see citing works
Frequency-Aware Attention based LSTM Networks for Cardiovascular Disease
Park, H. D., Han, Y., & Choi, J. H. (2018) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc.2018.8539509 · Cited by 18 · see citing works
Panicacci, S., Donati, M., Profili, F., Francesconi, P., & Fanucci, L. (2020) · Computers · doi:10.3390/computers10010004 · Cited by 15 · see citing works
The effects of deep network topology on mortality prediction
Du, H., Ghassemi, M. M., & Feng, M. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591263 · Cited by 14 · see citing works
Predicting Future Disorders via Temporal Knowledge Graphs and Medical Ontologies
Postiglione, M., Bean, D. M., Kraljević, Ž., Dobson, R., & Moscato, V. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3390419 · Cited by 9 · see citing works
Machine and deep learning techniques for the prediction of diabetics: a review
Modak, S. K. S., & Jha, V. K. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19766-9 · Cited by 10 · see citing works
Santosh Reddy, P., & Chandrasekar, M. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130218 · Cited by 10 · see citing works
Bajor, J. M., Mesa, D. A., Osterman, T. J., & Lasko, T. A. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1802.04233 · Cited by 9 · see citing works
A Novel Approach to Prognosticate CKD Using a Supervised and Unsupervised Learning Algorithms
Ashwathi, S., Goud, A. S., Niranjan, L., Sreekantha, B., & Suneetha, J. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-19-8497-6_11 · Cited by 8 · see citing works
Merugula, S., Padmaja, B., & Ragavi, V. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7355 · Cited by 7 · see citing works
Pradip, U., & Moghe, A. A. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104706 · Cited by 7 · see citing works
Analysis of the 72-h Mortality of Emergency Room Septic Patients Based on a Deep Belief Network
Perng, J., Kao, I.-H., Yen-Wei, C., Lai, Y.-H., Su, C.-M., Hung, S.-C., Lee, M. S.-S., & Kung, C. (2018) · IEEE Access · doi:10.1109/access.2018.2884509 · Cited by 7 · see citing works
Representation Learning for Electronic Health Records: A Survey
Chen, P. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1487/1/012015 · Cited by 6 · see citing works
Deep Learning for Healthcare Biometrics
Kumar, U., Tripathi, E., Tripathi, S. P., & Gupta, K. K. (2018) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-5225-7525-2.ch004 · Cited by 5 · see citing works
Huang, H., Shang, X., Zhao, H., Wu, N., Li, W., Xu, Y. F., Zhou, Y., & Fu, L. (2019) · IEEE Access · doi:10.1109/access.2019.2937892 · Cited by 5 · see citing works
Deep Holistic Representation Learning from EHR
Zhang, E., Robinson, R., & Pfahringer, B. (2018) · International Symposium on Medical Information and Communication Technology · doi:10.1109/ismict.2018.8573698 · Cited by 4 · see citing works
Chen, D., Shi, C., Wang, M., & Pan, Q. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70139-4_77 · Cited by 4 · see citing works
Deep learning for biomedical applications
Freitas, J. D., Glicksberg, B. S., Johnson, K. W., & Miotto, R. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00004-x · Cited by 4 · see citing works
Self-citationLee, S., Kim, S., Koh, G., & Ahn, H. (2024) · Journal of Personalized Medicine · doi:10.3390/jpm14080812 · Cited by 3 · see citing works
Reddy, P. S., & Chandrasekar, M. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-7330-6_64 · Cited by 3 · see citing works
Diabetes Mellitus Forecasting Using Population Health Data in Ontario, CanadaPreprint
Ravaut, M., Sadeghi, H., Leung, K. K., Volkovs, M., & Rosella, L. C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.04137 · Cited by 3 · see citing works
Beldi, A., Sassi, S., Chbeir, R., & Jemai, A. (2022) · Institution of Engineering and Technology eBooks · doi:10.1049/pbhe044e_ch5 · Cited by 2 · see citing works
AI in the Detection of Breast Cancer and Related Ethical and Legal Issues
Mukhopadhyay, K. D., Guha, T., & Basu, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-8457-8_9 · Cited by 2 · see citing works
NLICE: Synthetic Medical Record Generation for Effective Primary Healthcare Differential Diagnosis
Al-Ars, Z., Agba, O., Guo, Z., Boerkamp, C., Jaber, Z., & Jaber, T. (2023) · IEEE International Conference on Bioinformatics and Bioengineering (BIBE) · doi:10.1109/bibe60311.2023.00071 · Cited by 2 · see citing works
Xu, H. (2019) · International Journal of Chinese & Comparative Philosophy of Medicine · doi:10.24112/ijccpm.171664 · Cited by 1 · see citing works
Real-Time Medical Electronic Data Mining Based on Shapelet Pattern Recognition
Mao, Y., Li, Y., Han, J., & Chen, Y. (2022) · International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM) · doi:10.1109/mlccim55934.2022.00017 · Cited by 1 · see citing works
Zafar, I., Ahmad, S. U., Rather, M. A., Khan, A. A., Ain, Q. U., Safder, A., & Sehgal, S. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00002-3 · Cited by 1 · see citing works
Automatic retrieval of health case reports for public needs using deep learning techniques
Liu, Y.-H., Chen, S.-F., & Wen, D.-W. (2023) · Aslib Journal of Information Management · doi:10.1108/ajim-01-2023-0002 · Cited by 1 · see citing works
DG_summ: A schema-driven approach for personalized summarizing heterogeneous data graphs
Beldi, A., Sassi, S., Chbeir, R., & Jemai, A. (2023) · Computer Science and Information Systems · doi:10.2298/csis230331062b · Cited by 1 · see citing works
On Credibility of Medical Artificial Intelligence Technologies based on Physician-Patient Trust
Xu, H. (2020) · JOURNAL OF ENGINEERING STUDIES · doi:10.3724/sp.j.1224.2020.00252 · Cited by 1 · see citing works
Learning the impact of data pre-processing in data analysis
Bilalli, B. (2018) · doi:10.5821/dissertation-2117-120991 · Cited by 1 · see citing works
A Case Study on Hospital Readmission Prediction Using Deep Learning Algorithms on EHRs
Et.al, B. S. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i3.1718
Vineeth, R., Rithish, R., Varma, D. V. S. N. S., Gowda, N. M., & Prakash, B. V. A. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-0171-2_27
Analysis of Electronic Medical Records to Forecast Probable Disease Outbreaks in Bangladesh
Akter, K., Islam, M., & Kabir, K. H. (2021) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp52854.2021.9550984
Мабу, М. Э. (2024) · Sovremennye informacionnye tehnologii i IT-obrazovanie. · doi:10.25559/sitito.020.202401.175-181
A Deep Learning Model for Surface Defect Detection in Thermoelectric Cooler Components
Feng, W., Lu, Y. L., Shi, S., Li, M., & Ge, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9921-6_14
Forecasting Post-Harvest Tomato Shelf Life with Recurrent Neural Networks and Electronic Nose Data
Affognon, L., Ezin, E. C., & Diallo, C. (2025) · SN Computer Science · doi:10.1007/s42979-025-04310-9
Neural Networks Applied in Disease Prediction: A Systematic Review
García, G. E. R., Vázquez, J. C. R., López, M. J. C., Castillo, A. L., & Guzmán, I. O. J. (2026) · Intelligent systems reference library · doi:10.1007/978-3-032-13497-4_7
- Authors
- Miotto, R., Li, L., & Dudley, J. T.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Miotto, Riccardo
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 768-774
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2474925868
- Cited by
- 59
- References
- 12
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 8 · 2022: 8 · 2021: 7 · 2020: 6 · 2019: 8 · 2018: 8 · 2017: 4 · 2016: 2
- FWCI
- 3.95
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- electronic health records, deep learning, patient representation learning, preventive medicine, disease risk prediction, liver cancer, diabetes mellitus, heart failure
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