Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1179 · Cited by 25,545 · see citing works

Neural Machine Translation by Jointly Learning to Align and Translate

Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213 · see citing works

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

Advances in Neural Information Processing Systems 29

Teymur, O., Zygalakis, K., & Calderhead, B. (2016) · Cited by 13,881 · see citing works

Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups

Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A.-R., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., et al. (2012) · IEEE Signal Processing Magazine · doi:10.1109/msp.2012.2205597 · Cited by 10,260 · see citing works

MIMIC-III, a freely accessible critical care database

Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016) · Scientific Data · doi:10.1038/sdata.2016.35 · Cited by 8,141 · see citing works

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

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 · see citing works

The mythos of model interpretability

Lipton, Z. C. (2018) · Communications of the ACM · doi:10.1145/3233231 · Cited by 5,427 · see citing works

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Cho, K., van Merrienboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1406.1078 · Cited by 3,317 · 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) · npj Digital Medicine · doi:10.1038/s41746-018-0029-1 · Cited by 2,648 · 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 patient: An unsupervised representation to predict the future of patients from the electronic health records

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals

Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H., & Adeli, H. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.09.017 · Cited by 1,658 · see citing works

Understanding Black-box Predictions via Influence Functions

Koh, P. W., & Liang, P. (2017) · arXiv · doi:10.48550/arxiv.1703.04730 · Cited by 1,514 · see citing works

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 · 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

Developing a Test Collection for the Evaluation of Integrated Search

Lykke, M., Larsen, B., Lund, H., & Ingwersen, P. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-12275-0_63 · Cited by 970 · see citing works

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 · see citing works

Learning to Diagnose with LSTM Recurrent Neural Networks

Lipton, Z. C., Kale, D. C., Elkan, C., & Wetzel, R. (2015) · arXiv · doi:10.48550/arxiv.1511.03677 · Cited by 879 · 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

Explainable Prediction of Medical Codes from Clinical Text

Mullenbach, J., Wiegreffe, S., Duke, J., Sun, J., & Eisenstein, J. (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/n18-1100 · Cited by 597 · see citing works

Untitled work

(2016) · PubMed · Cited by 576 · see citing works

Real-valued (Medical) Time Series Generation with Recurrent Conditional GANsPreprint

Esteban, C., Hyland, S. L., & Rätsch, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.02633 · Cited by 567 · see citing works

Partial Transfer Learning with Selective Adversarial Networks

Cao, Z., Long, M., Wang, J., & Jordan, M. I. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00288 · Cited by 507 · see citing works

Deep 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

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 · see citing works

Generating Multi-label Discrete Patient Records using Generative Adversarial NetworksPreprint

Choi, E., Biswal, S., Malin, B., Duke, J. D., Stewart, W. F., & Sun, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06490 · Cited by 422 · 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 412 · see citing works

De-identification of patient notes with recurrent neural networks

Dernoncourt, F., Lee, J. Y., Uzuner, Ö., & Peter Szolovits, B. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw156 · Cited by 389 · see citing works

Ensemble of Feature:based and Deep learning:based Classifiers for Detection of Abnormal Heart Sounds

Potes, C., Parvaneh, S., Rahman, A., & Conroy, B. (2016) · Computing in cardiology · doi:10.22489/cinc.2016.182-399 · Cited by 368 · see citing works

Bidirectional RNN for Medical Event Detection in Electronic Health Records

Jagannatha, A. N., & Yu, H. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1056 · Cited by 314 · see citing works

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

Lasko, T. A., Denny, J. C., & Levy, M. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066341 · Cited by 313 · see citing works

Opportunities and obstacles for deep learning in biology and medicinePreprint

Ching, T. H., Himmelstein, D. S., Beaulieu‐Jones, B. K., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142760 · Cited by 308 · see citing works

A comparison of models for predicting early hospital readmissions

Futoma, J., Morris, J. D., & Lucas, J. E. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.05.016 · Cited by 284 · see citing works

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-31750-2_3 · Cited by 269 · see citing works

Learning Low-Dimensional Representations of Medical Concepts.

Choi, Y., Chiu, C. Y.-I., & Sontag, D. A. (2016) · PubMed Central · Cited by 256 · 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

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 · 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

MISSING DATA IMPUTATION IN THE ELECTRONIC HEALTH RECORD USING DEEPLY LEARNED AUTOENCODERS

Beaulieu‐Jones, B. K., & Moore, J. H. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0021 · Cited by 194 · see citing works

Semi-supervised learning of the electronic health record for phenotype stratification

Beaulieu‐Jones, B. K., & Greene, C. S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.007 · Cited by 175 · see citing works

Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

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 · see citing works

Recurrent neural networks for classifying relations in clinical notes

Luo, Y. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.07.006 · Cited by 154 · see citing works

De-identification of clinical notes via recurrent neural network and conditional random field

Liu, Z., Tang, B., Wang, X., & Chen, Q. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.05.023 · Cited by 153 · see citing works

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports

Qiu, J. X., Yoon, H., Fearn, P., & Tourassi, G. D. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2700722 · Cited by 146 · see citing works

SLEEPNET: Automated Sleep Staging System via Deep LearningPreprint

Biswal, S., Kulas, J. A., Sun, H., Goparaju, B., Westover, M. B., Bianchi, M. T., & Sun, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.08262 · Cited by 134 · see citing works

Multi-Label Classification of Patient Notes a Case Study on ICD Code AssignmentPreprint

Baumel, T., Nassour-Kassis, J., Cohen, R., Elhadad, M., & Elhadad, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.09587 · Cited by 134 · see citing works

Learning to Detect Sepsis with a Multitask Gaussian Process RNN ClassifierPreprint

Futoma, J., Hariharan, S., & Heller, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04152 · Cited by 122 · see citing works

Towards Automated ICD Coding Using Deep LearningPreprint

Shi, H., Xie, P., Hu, Z., Zhang, M., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04075 · Cited by 120 · see citing works

Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data

Liu, Y., Logan, B. R., Liu, N., Xu, Z., Tang, J., & Wang, Y. (2017) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2017.45 · Cited by 112 · see citing works

Distilling Knowledge from Deep Networks with Applications to Healthcare DomainPreprint

Che, Z., Purushotham, S., Khemani, R. G., & Liu, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1512.03542 · Cited by 109 · see citing works

Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks

Bradshaw, J., Matthews, A. G. D. G., & Ghahramani, Z. (2017) · arXiv · doi:10.48550/arxiv.1707.02476 · Cited by 109 · see citing works

Clinical phenotyping in selected national networks: demonstrating the need for high-throughput, portable, and computational methods

Richesson, R., Sun, J., Pathak, J., Kho, A., & Denny, J. C. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.05.005 · Cited by 106 · see citing works

Identifying adverse drug event information in clinical notes with distributional semantic representations of context

Henriksson, A., Kvist, M., Dalianis, H., & Duneld, M. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.08.013 · Cited by 98 · see citing works

An Improved Multi-Output Gaussian Process RNN with Real-Time Validation for Early Sepsis DetectionPreprint

Futoma, J., Hariharan, S., Sendak, M., Brajer, N., Clement, M. E., Bedoya, A., O’Brien, C. L., & Heller, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.05894 · Cited by 98 · see citing works

A Regularized Deep Learning Approach for Clinical Risk Prediction of Acute Coronary Syndrome Using Electronic Health Records

Huang, Z., Dong, W., Duan, H., & Liu, J. (2017) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2017.2731158 · Cited by 94 · see citing works

Clinical Intervention Prediction and Understanding using Deep NetworksPreprint

Suresh, H., Hunt, N. J., Johnson, A. E. W., Celi, L. A., Peter Szolovits, B., & Ghassemi, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08498 · Cited by 81 · see citing works

Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes

Luo, Y., Cheng, Y., Uzuner, Ö., Peter Szolovits, B., & Starren, J. B. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx090 · Cited by 74 · see citing works

Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning

Lin, Q., Ye, S.-Q., Huang, X., Li, S.-Y., Zhang, M., Xue, Y., & Chen, W.-S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42297-8_74 · Cited by 73 · see citing works

Automated disease cohort selection using word embeddings from Electronic Health Records

Glicksberg, B. S., Miotto, R., Johnson, K. W., Shameer, K., Li, L., Chen, R., & Dudley, J. T. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0014 · Cited by 66 · see citing works

Word2Vec inversion and traditional text classifiers for phenotyping lupus

Turner, C. A., Jacobs, A. D., Marques, C. K., Oates, J. C., Kamen, D. L., Anderson, P. E., & Obeid, J. S. (2017) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-017-0518-1 · Cited by 65 · see citing works

DeepIED: An epileptic discharge detector for EEG-fMRI based on deep learning

Hao, Y., Khoo, H. M., von Ellenrieder, N., Zazubovits, N., & Gotman, J. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.12.005 · Cited by 64 · see citing works

Partial Transfer Learning with Selective Adversarial NetworksPreprint

Cao, Z., Long, M., Wang, J., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.07901 · Cited by 62 · see citing works

Deep Learning to Predict Patient Future Diseases from the Electronic Health Records

Miotto, R., Li, L., & Dudley, J. T. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-30671-1_66 · Cited by 59 · see citing works

Clinical Relation Extraction with Deep Learning

Lv, X., Guan, Y., Yang, J., & Wu, J. (2016) · International Journal of Hybrid Information Technology · doi:10.14257/ijhit.2016.9.7.22 · Cited by 58 · see citing works

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age

Wang, Z., Li, L., Glicksberg, B. S., Israel, A., Dudley, J. T., & Ma’ayan, A. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.11.003 · Cited by 51 · see citing works

Temporal Convolutional Neural Networks for Diagnosis from Lab TestsPreprint

Razavian, N., & Sontag, D. A. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.07938 · Cited by 49 · see citing works

Neural Document Embeddings for Intensive Care Patient Mortality PredictionPreprint

Grnarova, P., Schmidt, F., Hyland, S. L., & Eickhoff, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00467 · Cited by 46 · see citing works

Deep Counterfactual Networks with Propensity-DropoutPreprint

Alaa, A. M., Weisz, M., & van der Schaar, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.05966 · Cited by 44 · see citing works

Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature EmbeddingPreprint

Che, Z., Cheng, Y., Sun, Z., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.07474 · Cited by 41 · see citing works

Applying deep learning on electronic health records in Swedish to predict healthcare-associated infections

Jacobson, O., & Dalianis, H. (2016) · Workshop on Biomedical Natural Language Processing (BioNLP) · doi:10.18653/v1/w16-2926 · Cited by 38 · see citing works

Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test TrajectoriesPreprint

Yahi, A., Vanguri, R. S., Elhadad, N., & Tatonetti, N. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1712.00164 · Cited by 34 · see citing works

Intelligent Computing Methodologies

Huang, D.-S., Jo, K.-H., & Wang, L. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09339-0 · Cited by 33 · see citing works

Comparing Rule-Based and Deep Learning Models for Patient PhenotypingPreprint

Gehrmann, S., Dernoncourt, F., Li, Y., Carlson, E. T., Wu, J. T.-Y., Welt, J. A., Foote, J., Moseley, E. T., Grant, D. W., Tyler, P. D., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.08705 · Cited by 22 · see citing works

The Use of Autoencoders for Discovering Patient PhenotypesPreprint

Suresh, H., Szolovits, P., & Ghassemi, M. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07004 · Cited by 20 · see citing works

Grounded Recurrent Neural NetworksPreprint

Vani, A., Jernite, Y., & Sontag, D. A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.08557 · Cited by 19 · see citing works

Predicting Adolescent Suicide Attempts with Neural NetworksPreprint

Bhat, H. S., & Goldman-Mellor, S. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10057 · Cited by 18 · see citing works

Multi-Label Learning from Medical Plain Text with Convolutional Residual ModelsPreprint

Zhang, X., Henao, R., Gan, Z., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05062 · Cited by 17 · see citing works

Learning Effective Representations from Clinical Notes.

Dubois, S., Romano, N., Shah, N. H., et al. (2017) · Cited by 16 · 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

Medical Diagnosis From Laboratory Tests by Combining Generative and Discriminative LearningPreprint

Zhang, S., Xie, P., Wang, D., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04329 · Cited by 12 · see citing works

Disease Prediction from Electronic Health Records Using Generative Adversarial Networks.Preprint

Hwang, U., Choi, S., & Yoon, S. (2017) · arXiv (Cornell University) · Cited by 11 · see citing works

Effective Representations of Clinical NotesPreprint

Dubois, S., Romano, N., Kale, D. C., Shah, N. H., & Jung, K. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.07025 · Cited by 11 · see citing works

Advances in Big Data

Angelov, P., Manolopoulos, Y., Iliadis, L., et al. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-47898-2 · Cited by 9 · see citing works

Deep Learning from EEG Reports for Inferring Underspecified Information.

Goodwin, T. R., & Harabagiu, S. M. (2017) · PubMed · Cited by 9 · see citing works

Learning compressed representations of blood samples time series with missing dataPreprint

Bianchi, F. M., Mikalsen, K. Ø., & Jenssen, R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.07547 · Cited by 5 · see citing works

Finding Algebraic Structure of Care in Time: A Deep Learning ApproachPreprint

Nguyen, P., Tran, T., & Venkatesh, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.07980 · Cited by 4 · see citing works

Predicting Discharge Medications at Admission Time Based on Deep LearningPreprint

Yuan, Y., Xie, P., Gao, X., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.01386 · Cited by 3 · see citing works

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058 · see citing works

Deep learning modelling techniques: Current progress, applications, advantages, and challenges

Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10466-8 · Cited by 1,079 · see citing works

Artificial intelligence-enabled healthcare delivery

Reddy, S., Fox, J., & Purohit, M. P. (2019) · Journal of the Royal Society of Medicine · doi:10.1177/0141076818815510 · Cited by 635 · see citing works

Explainable artificial intelligence model to predict acute critical illness from electronic health records

Lauritsen, S. M., Kristensen, M., Olsen, M. V., Larsen, M. S., Lauritsen, K. M., Jørgensen, M. J., Lange, J., & Thiesson, B. (2020) · Nature Communications · doi:10.1038/s41467-020-17431-x · Cited by 448 · see citing works

Explainable, trustworthy, and ethical machine learning for healthcare: A survey

Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372 · see citing works

The shaky foundations of large language models and foundation models for electronic health records

Wornow, M., Xu, Y., Thapa, R., Patel, B., Steinberg, E., Fleming, S., Pfeffer, M. A., Fries, J., & Shah, N. H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00879-8 · Cited by 324 · see citing works

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011126 · Cited by 345 · see citing works

Artificial Intelligence in Digital Pathology: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy

McGenity, C., Clarke, E. L., Jennings, C., Matthews, G., Cartlidge, C., Freduah-Agyemang, H., Stocken, D. D., & Treanor, D. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01106-8 · Cited by 290 · 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

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

Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges: Primary Health Care Informatics Working Group Contribution to the Yearbook of Medical Informatics 2019

Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., & de Lusignan, S. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677901 · Cited by 200 · see citing works

Bridging the implementation gap of machine learning in healthcare

Seneviratne, M. G., Shah, N. H., & Chu, L. (2020) · BMJ Innovations · doi:10.1136/bmjinnov-2019-000359 · Cited by 173 · see citing works

A Path for Translation of Machine Learning Products into Healthcare Delivery

(2020) · EMJ Innovations · doi:10.33590/emjinnov/19-00172 · Cited by 172 · see citing works

Limitations of transformers on clinical text classification

Gao, S., Alawad, M., Young, M. T., Gounley, J., Schaefferkoetter, N., Yoon, H. J., Wu, X.-C., Durbin, E. B., Doherty, J., Stroup, A., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3062322 · Cited by 157 · 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

Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis

Amiri, H., Peiravi, S., rezazadeh shojaee, S. S., Rouhparvarzamin, M., Nateghi, M. N., Etemadi, M. H., ShojaeiBaghini, M., Musaie, F., Anvari, M. H., & Asadi Anar, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05406-1 · Cited by 149 · see citing works

A framework for the oversight and local deployment of safe and high-quality prediction models

Bedoya, A. D., Economou-Zavlanos, N. J., Goldstein, B. A., Young, A., Jelovsek, J. E., O’Brien, C., Parrish, A. B., Elengold, S., Lytle, K., Balu, S., et al. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac078 · Cited by 119 · see citing works

Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Long, G., Shen, T., Tan, Y., Gerrard, L., Clarke, A., & Jiang, J. (2022) · Humanity Driven AI · doi:10.1007/978-3-030-72188-6_6 · Cited by 85 · see citing works

FairLens: Auditing black-box clinical decision support systems

Panigutti, C., Perotti, A., Panisson, A., Bajardi, P., & Pedreschi, D. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102657 · Cited by 86 · see citing works

A study of deep learning methods for de-identification of clinical notes in cross-institute settings

Yang, X., Lyu, T., Li, Q., Lee, C.-Y., Bian, J., Hogan, W. R., & Wu, Y. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0935-4 · Cited by 73 · see citing works

Recent Advances in Artificial Intelligence and Wearable Sensors in Healthcare Delivery

Junaid, S. B., Imam, A. A., Abdulkarim, M., Surakat, Y. A., Balogun, A. O., Kumar, G., Shuaibu, A. N., Garba, A., Sahalu, Y., Mohammed, A., et al. (2022) · Applied Sciences · doi:10.3390/app122010271 · Cited by 65 · see citing works

Early prediction of diagnostic-related groups and estimation of hospital cost by processing clinical notes

Liu, J., Capurro, D., Nguyen, A., & Verspoor, K. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00474-9 · Cited by 50 · see citing works

Enhancing precision in detecting severe immune-related adverse events: comparative analysis of large language models and international classification of disease codes in patient records

Sun, V. H., Heemelaar, J. C., Hadzic, I., Raghu, V. K., Wu, C.-Y., Zubiri, L., Ghamari, A., LeBoeuf, N. R., Abu-Shawer, O., Kehl, K. L., et al. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.24.00326 · Cited by 38 · see citing works

Treatment effect prediction with adversarial deep learning using electronic health records

Chu, J., Dong, W., Wang, J., He, K., & Huang, Z. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01151-9 · Cited by 24 · see citing works

On Missingness Features in Machine Learning Models for Critical Care: Observational Study

Singh, J., Sato, M., & Ohkuma, T. (2021) · JMIR Medical Informatics · doi:10.2196/25022 · Cited by 17 · see citing works

A Self-Supervised Framework for Laboratory Data Imputation in Electronic Health Records

Heilbroner, S. P., Carter, C., Vidmar, D. M., Mueller, E. T., Stumpe, M. C., & Miotto, R. (2025) · Communications Medicine · doi:10.1038/s43856-025-00973-w · Cited by 1 · see citing works

Towards the development of a learning analytics extension in open edX

Ruiz, J. S., Pijeira‐Díaz, H. J., Ruipérez‐Valiente, J. A., et al. (2014) · Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'21) · doi:10.1145/2669711.2669914 · Cited by 34 · see citing works

Challenges of Learning Analytics Execution in the Educational System

Singh, M. K. (2018) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-5369-4.ch003

The Role of Learning Analytics in Global Higher Education

Kasemsap, K. (2016) · Advances in educational marketing, administration, and leadership book series · doi:10.4018/978-1-4666-9983-0.ch012 · Cited by 20 · see citing works

Combining Learning Analytics and the Flipped Classroom in a MOOC of maths

Redondo, D., Muñoz‐Merino, P. J., Ruipérez‐Valiente, J. A., Kloos, C. D., Pijeira‐Díaz, H. J., & Ruiz, J. S. (2015) · IMDEA Networks Institute Digital Repository · Cited by 6 · see citing works

A Study on the Impact of Learning Analytics to Evaluate Students' Performance in Higher Education

Singh, N. (2018) · Advances in educational technologies and instructional design book series · doi:10.4018/978-1-5225-5369-4.ch002