Mining Electronic Health Records (EHRs): A Survey

Nonparametric Estimation from Incomplete Observations

Kaplan, E. L., & Meier, P. (1992) · Springer series in statistics · doi:10.1007/978-1-4612-4380-9_25 · Cited by 45,239

Nonparametric Estimation from Incomplete Observations

Kaplan, E. L., & Meier, P. (1958) · Journal of the American Statistical Association · doi:10.1080/01621459.1958.10501452 · Cited by 39,158

Regression Models and Life-Tables

Cox, D. R. (1992) · Springer Series in Statistics · doi:10.1007/978-1-4612-4380-9_37 · Cited by 26,297

Statistical Aspects of the Analysis of Data From Retrospective Studies of Disease

(1959) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/22.4.719 · Cited by 14,689

Fast algorithms for mining association rules

Agrawal, R., & Srikant, R. (1998) · Cited by 11,362

Random-Effects Models for Longitudinal Data

Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923

Support vector machines

Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243

Introduction to Data Mining

Chiu, S., & Tavella, D. (2008) · Routledge eBooks · doi:10.4324/9780080878096-12 · Cited by 6,987

Untitled work

Schulz (1995) · doi:10.1001/jama.1995.03520290060030 · Cited by 5,634

Marginal Structural Models and Causal Inference in Epidemiology

Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581

Synthesizing electronic health records using improved generative adversarial networks

Baowaly, M. K., Lin, C.-C., Liu, C.-L., & Chen, K.-T. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy142 · Cited by 285

A systematic review of the prediction of hospital length of stay: Towards a unified framework

Stone, K., Reyer Zwiggelaar, A., Jones, P., & Parthaláin, N. M. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000017 · Cited by 274

Analyzing the performance of a blockchain-based personal health record implementation

Roehrs, A., da Costa, C. A., Righi, R. D. R., da Silva, V. F., Goldim, J. R., & Schmidt, D. C. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103140 · Cited by 185

Pre-trained Language Models in Biomedical Domain: A Systematic Survey

Wang, B., Xie, Q., Pei, J., Chen, Z., Tiwari, P., Li, Z., & Fu, J. (2023) · ACM Computing Surveys · doi:10.1145/3611651 · Cited by 238

Data Integration Challenges for Machine Learning in Precision Medicine

Martínez-García, M., & Hernández-Lemus, E. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.784455 · Cited by 185

Doctor XAI

Panigutti, C., Perotti, A., & Pedreschi, D. (2020) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3351095.3372855 · Cited by 172

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

Diabetes Disease Prediction Using Machine Learning on Big Data of Healthcare

Mir, A., & Dhage, S. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697439 · Cited by 157

The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges

Sarwar, T., Seifollahi, S., Chan, J., Zhang, X., Aksakallı, V., Hudson, I. L., Verspoor, K. M., & Cavedon, L. (2022) · ACM Computing Surveys · doi:10.1145/3490234 · Cited by 151

Privacy preserving Federated Learning framework for IoMT based big data analysis using edge computing

Nair, A. K., Sahoo, J., & Raj, E. D. (2023) · Computer Standards & Interfaces · doi:10.1016/j.csi.2023.103720 · Cited by 120

Deep Generative Models for Synthetic Data: A Survey

Eigenschink, P., Reutterer, T., Vamosi, S., Vamosi, R., Sun, C., & Kalcher, K. (2023) · IEEE Access · doi:10.1109/access.2023.3275134 · Cited by 122

Building more accurate decision trees with the additive tree

Luna, J. M., Gennatas, E. D., Ungar, L., Eaton, E., Diffenderfer, E. S., Jensen, S. T., Simone, C. B., Friedman, J. H., Solberg, T. D., & Valdés, G. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816748116 · Cited by 101

Zero-shot information extraction from radiological reports using ChatGPT

Hu, D., Liu, B., Zhu, X., Lü, X., & Wu, N. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105321 · Cited by 95

The Revival of the Notes Field: Leveraging the Unstructured Content in Electronic Health Records

Assale, M., Dui, L. G., Cina, A., Seveso, A., & Cabitza, F. (2019) · Frontiers in Medicine · doi:10.3389/fmed.2019.00066 · Cited by 100

Application of Bayesian networks to generate synthetic health data

Kaur, D., Sobiesk, M., Patil, S. S., Liu, J., Bhagat, P., Gupta, A. D., & Markuzon, N. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa303 · Cited by 101

Multi-Time Attention Networks for Irregularly Sampled Time Series

Shukla, S. N., & Marlin, B. M. (2021) · PubMed · doi:10.48550/arxiv.2101.10318 · Cited by 192

Deep learning for heterogeneous medical data analysis

Yue, L., Tian, D., Chen, W., Han, X., & Yin, M. (2020) · World Wide Web · doi:10.1007/s11280-019-00764-z · Cited by 85

Big data analytics in healthcare

Guo, C., & Chen, J. (2023) · Translational Systems Sciences · doi:10.1007/978-981-99-1075-5_2 · Cited by 90

Federated Learning in Health care Using Structured Medical Data

Oh, W., & Nadkarni, G. N. (2023) · Advances in Kidney Disease and Health · doi:10.1053/j.akdh.2022.11.007 · Cited by 88

Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time Series

Mulyadi, A. W., Jun, E., & Suk, H. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3053599 · Cited by 83

Using sequences of life-events to predict human lives

Savcisens, G., Eliassi‐Rad, T., Hansen, L. K., Mortensen, L. H., Lilleholt, L., Rogers, A., Zettler, I., & Lehmann, S. (2023) · Nature Computational Science · doi:10.1038/s43588-023-00573-5 · Cited by 99

Hyperledger Fabric Blockchain: Secure and Efficient Solution for Electronic Health Records

Uddin, M., Memon, M. S., Memon, I., Ali, I., Memon, J., Abdelhaq, M., & Alsaqour, R. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.015354 · Cited by 81

A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records

Ghosheh, G. O., Li, J., & Zhu, T. (2024) · ACM Computing Surveys · doi:10.1145/3636424 · Cited by 95

Early hospital mortality prediction using vital signals

Sadeghi, R., Banerjee, T., & Romine, W. (2018) · Smart Health · doi:10.1016/j.smhl.2018.07.001 · Cited by 63

Early Hospital Mortality Prediction Using Vital Signals

Sadeghi, R., Banerjee, T., & Romine, W. L. (2019) · Journal of Bioresource Management · Cited by 62

Health information technology and hospital performance the role of health information quality in teaching hospitals

Alolayyan, M. N., Alyahya, M. S. I., AlAlawin, A. H., Shoukat, A., & Nusairat, F. T. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05040 · Cited by 58

A Treatment Engine by Predicting Next-Period Prescriptions

Jin, B., Yang, H., Sun, L., Liu, C., Qu, Y., & Tong, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220095 · Cited by 59

An ensemble of neural models for nested adverse drug events and medication extraction with subwords

Ju, M., Nguyen, N. T. H., Miwa, M., & Ananiadou, S. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz075 · Cited by 55

Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

Aljrees, T. (2024) · PLoS ONE · doi:10.1371/journal.pone.0295632 · Cited by 58

Few-shot Domain Adaptation by Causal Mechanism TransferPreprint

Teshima, T., Sato, I., & Sugiyama, M. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2002.03497 · Cited by 53

Semantic Information Retrieval on Medical Texts

Tamine, L., & Goeuriot, L. (2021) · ACM Computing Surveys · doi:10.1145/3462476 · Cited by 65

SemClinBr - a multi-institutional and multi-specialty semantically annotated corpus for Portuguese clinical NLP tasks

Oliveira, L. E. S. E., Peters, A. C. D. C., da Silva, A. M. P., Gebeluca, C. P., Gumiel, Y. B., Cintho, L. M. M., Carvalho, D. R., Hasan, S. A., & Moro, C. M. C. (2022) · Journal of Biomedical Semantics · doi:10.1186/s13326-022-00269-1 · Cited by 48

Precision Medicine Informatics: Principles, Prospects, and Challenges

Afzal, M., Islam, S. M. R., Hussain, M., & Lee, S. (2020) · IEEE Access · doi:10.1109/access.2020.2965955 · Cited by 50

An evaluation of synthetic data augmentation for mitigating covariate bias in health data

Juwara, L., El‐Hussuna, A., & Emam, K. E. (2024) · Patterns · doi:10.1016/j.patter.2024.100946 · Cited by 58

Identifying Stroke Indicators Using Rough Sets

Pathan, M. S., Zhang, J., John, D., Nag, A., & Dev, S. (2020) · IEEE Access · doi:10.1109/access.2020.3039439 · Cited by 45

Using machine learning for predicting cervical cancer from Swedish electronic health records by mining hierarchical representations

Weegar, R., & Sundström, K. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237911 · Cited by 43

Big Data Analytics in Healthcare: Data-Driven Methods for Typical Treatment Pattern Mining

Guo, C., & Chen, J. (2019) · Journal of Systems Science and Systems Engineering · doi:10.1007/s11518-019-5437-5 · Cited by 42

A data-driven framework of typical treatment process extraction and evaluation

Chen, J., Sun, L., Guo, C., Wei, W., & Xie, Y. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.06.004 · Cited by 42

Modeling multivariate clinical event time-series with recurrent temporal mechanisms

Lee, J. M., & Hauskrecht, M. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102021 · Cited by 43

Personalized method for self-management of trunk postural ergonomic hazards in construction rebar ironwork

Yan, X., Li, H., Zhang, H., & Rose, T. M. (2018) · Advanced Engineering Informatics · doi:10.1016/j.aei.2018.04.013 · Cited by 39

Machine-Learning-Based Digital Twin System for Predicting the Progression of Prostate Cancer

Kim, J. K., Lee, S., Hong, S., & Choi, I.-Y. (2022) · Applied Sciences · doi:10.3390/app12168156 · Cited by 39

PPAD: a deep learning architecture to predict progression of Alzheimer’s disease

Olaimat, M. A., Martinez, J., Saeed, F., Bozdag, S., & Initiative, A. D. N. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad249 · Cited by 37

Pre-trained Language Models in Biomedical Domain: A Systematic SurveyPreprint

Wang, B., Xie, Q., Pei, J., Chen, Z., Tiwari, P., Li, Z., & Fu, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.05006 · Cited by 34

Procedure prediction from symbolic Electronic Health Records via time intervals analytics

Moskovitch, R., Polubriaginof, F. C. G., Weiss, A., Ryan, P. B., & Tatonetti, N. P. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.07.018 · Cited by 33

Early Prediction of Diabetes Complications from Electronic Health Records: A Multi-Task Survival Analysis Approach

Liu, B., Li, Y., Sun, Z., Ghosh, S., & Ng, K. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11290 · Cited by 33

Complication Risk Profiling in Diabetes Care: A Bayesian Multi-Task and Feature Relationship Learning Approach

Liu, B., Li, Y., Ghosh, S., Sun, Z., Ng, K., & Hu, J. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2904060 · Cited by 33

Making the COVID-19 Pandemic a Driver for Digital Health: Brazilian Strategies

Donida, B., da Costa, C. A., & Scherer, J. N. (2021) · JMIR Public Health and Surveillance · doi:10.2196/28643 · Cited by 59

A deep learning-based multi-turn conversation modeling for diagnostic Q&A document recommendation

Yang, Z., Xu, W. X., & Chen, R. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2020.102485 · Cited by 33

Research and Application of Artificial Intelligence Based on Electronic Health Records of Patients With Cancer: Systematic Review

Yang, X., Mu, D., Peng, H., Li, H., Wang, Y., Wang, P., Wang, Y., & Han, S. (2022) · JMIR Medical Informatics · doi:10.2196/33799 · Cited by 29

Time-aware Context-Gated Graph Attention Network for Clinical Risk Prediction

Xu, Y., Ying, H., Qian, S., Zhuang, F., Zhang, X., Wang, D., Wu, J., & Xiong, H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3181780 · Cited by 30

TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records

Al Olaimat, M., Bozdag, S., & Initiative, F. T. A. D. N. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae264 · Cited by 30

Distilling the knowledge from large-language model for health event prediction

Ding, S., Ye, J., Hu, X., & Zou, N. (2024) · Scientific Reports · doi:10.1038/s41598-024-75331-2 · Cited by 28

Transformers for extracting breast cancer information from Spanish clinical narratives

Pabón, O. S., Montenegro, O., García-Barragán, Á., Torrente, M., Provencio, M., Menasalvas, E., & Robles, V. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102625 · Cited by 31

Knowledge Graph-Based Clinical Decision Support System Reasoning: A Survey

Xiang, X., Wang, Z., Jia, Y., & Fang, B. (2019) · IEEE International Conference on Data Science in Cyberspace · doi:10.1109/dsc.2019.00063 · Cited by 28

Diagnosis of Gastric Cancer Using Machine Learning Techniques in Healthcare Sector: A Survey

Jamil, D., Palaniappan, S., Lokman, A., Naseem, M., & Zia, S. S. (2022) · Informatica · doi:10.31449/inf.v45i7.3633 · Cited by 25

What Can Knowledge Bring to Machine Learning?—A Survey of Low-shot Learning for Structured Data

Hu, Y., Chapman, A. P., Wen, G., & Hall, D. W. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3510030 · Cited by 28

Electronic Health Record Data in Cancer Learning Health Systems: Challenges and Opportunities

Post, A. R., Burningham, Z. R., & Halwani, A. S. (2022) · JCO Clinical Cancer Informatics · doi:10.1200/cci.21.00158 · Cited by 29

A Framework for User-Focused Electronic Health Record System Leveraging Hyperledger Fabric

Ndzimakhwe, M., Telukdarie, A., Munien, I., Vermeulen, A., Chude-Okonkwo, U. A. K., & Philbin, S. P. (2023) · Information · doi:10.3390/info14010051 · Cited by 26

UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction

Tan, Q., Ma, A. J., Ye, M., Yang, B., Deng, H., Wong, V. W., Tse, Y., Yip, T. C., Wong, G. L., Ching, J. Y., et al. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3357884 · Cited by 23

Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records

Soudan, B., Dandachi, F. F., & Nassif, A. B. (2022) · Smart Health · doi:10.1016/j.smhl.2022.100294 · Cited by 26

Integrating domain knowledge for biomedical text analysis into deep learning: A survey

Cai, L., Li, J., Lv, H., Liu, W., Niu, H., & Wang, Z. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104418 · Cited by 25

Cervical cancer detection using K nearest neighbor imputer and stacked ensemble learningmodel

Chen, X., Aljrees, T., Umer, M., Saidani, O., Almuqren, L., Mzoughi, O., Ishaq, A., & Ashraf, I. (2023) · Digital Health · doi:10.1177/20552076231203802 · Cited by 25

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records

Wang, G., Lou, X., Guo, F., Kwok, D., & Cao, C. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3346210 · Cited by 25

Uncertainty-Gated Stochastic Sequential Model for EHR Mortality Prediction

Jun, E., Mulyadi, A. W., Choi, J. H., & Suk, H. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3016670 · Cited by 22

Incorporating real-world evidence into the development of patient blood glucose prediction algorithms for the ICU

Fitzgerald, O., Perez‐Concha, O., Gallego, B., Saxena, M. K., Rudd, L., Metke‐Jimenez, A., & Jorm, L. R. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab060 · Cited by 25

Iterative Annotation of Biomedical NER Corpora with Deep Neural Networks and Knowledge Bases

Silvestri, S., Gargiulo, F., & Ciampi, M. (2022) · Applied Sciences · doi:10.3390/app12125775 · Cited by 24

Machine learning for administrative health records: A systematic review of techniques and applications

Caruana, A., Bandara, M., Musiał, K., Catchpoole, D. R., & Kennedy, P. J. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102642 · Cited by 27

Distributed Tensor Decomposition for Large Scale Health Analytics

He, H., Henderson, J., & Ho, J. C. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313548 · Cited by 22

Development and Validation of a Dynamic Real-Time Risk Prediction Model for Intensive Care Units Patients Based on Longitudinal Irregular Data: Multicenter Retrospective Study

Zheng, Z. Q., Luo, J., Zhu, Y., Du, L., Lan, L., Zhou, X., Yang, X., & Huang, S. (2025) · Journal of Medical Internet Research · doi:10.2196/69293 · Cited by 29

Transformer patient embedding using electronic health records enables patient stratification and progression analysis

Su, X., Grabowska, M. E., Kullo, I. J., Luo, Y., Smoller, J. W., Walunas, T. L., Wei, W., Jarvik, G. P., Mooney, S. D., & Crosslin, D. R. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01872-z · Cited by 30

Predictive Modeling of Clinical Events with Mutual Enhancement Between Longitudinal Patient Records and Medical Knowledge Graph

Xiao, X., Xu, X., Sun, Y., Liu, X., Li, X., Xie, G., & Wang, F. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00089 · Cited by 21

A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study

Zhang, X., Xue, Y., Su, X., Chen, S., Liu, K., Chen, W., Liu, M., & Hu, Y. Q. (2022) · JMIR Medical Informatics · doi:10.2196/38053 · Cited by 20

Identification and Characterization of Immune Checkpoint Inhibitor–Induced Toxicities From Electronic Health Records Using Natural Language Processing

Barman, H., Venkateswaran, S., Santo, A. D., Yoo, U., Silvert, E., Rao, K., Raghunathan, B., Kottschade, L. A., Block, M. S., Chandler, G. S., et al. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00151 · Cited by 20

A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging

Aggarwal, K., Khadanga, S., Joty, S., Kazaglis, L., & Srivastava, J. (2018) · 2018 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata.2018.8622286 · Cited by 20

Extraction of actionable knowledge to reduce hospital readmissions through patients personalization

Mardini, M., & Raś, Z. W. (2019) · Information Sciences · doi:10.1016/j.ins.2019.02.006 · Cited by 20

Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning

Greenbury, S. F., Ougham, K., Wu, J., Battersby, C., Gale, C., Modi, N., & Angelini, E. D. (2021) · Scientific Reports · doi:10.1038/s41598-021-85878-z · Cited by 20

Mining Electronic Health Records: A SurveyPreprint

Yadav, P., Steinbach, M. S., Kumar, V., & Simon, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.03222 · Cited by 20

iHELP: Personalised Health Monitoring and Decision Support Based on Artificial Intelligence and Holistic Health Records

Manias, G., den Akker, H. O., Azqueta-Alzúaz, A., Burgos, D., Capocchiano, N. D., Crespo, B. L., Dalianis, A., Damiani, A., Filipov, K., Giotis, G., et al. (2021) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc53001.2021.9631475 · Cited by 20

Prediction of acute hypertensive episodes in critically ill patients

Itzhak, N., Pessach, I. M., & Moskovitch, R. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102525 · Cited by 19

SUSTain

Perros, I., Papalexakis, E. E., Park, H., Vuduc, R. W., Yan, X. S., deFilippi, C. R., Stewart, W. F., & Sun, J. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3219999 · Cited by 19

A maximum likelihood approach to electronic health record phenotyping using positive and unlabeled patients

Zhang, L., Ding, X., Yanyuan Ma, B., Muthu, N., Ajmal, I., Moore, J. H., Herman, D. S., & Chen, J. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz170 · Cited by 19

Weight-based multiple empirical kernel learning with neighbor discriminant constraint for heart failure mortality prediction

Wang, Z., Wang, B., Zhou, Y., Li, D., & Yin, Y. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103340 · Cited by 19

Explaining Multi-label Black-Box Classifiers for Health Applications

Panigutti, C., Guidotti, R., Monreale, A., & Pedreschi, D. (2019) · Studies in computational intelligence · doi:10.1007/978-3-030-24409-5_9 · Cited by 19

Cross-Domain Missingness-Aware Time-Series Adaptation With Similarity Distillation in Medical Applications

Yang, B., Ye, M., Tan, Q., & Yuen, P. C. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.3011934 · Cited by 19

tsflex: Flexible time series processing & feature extraction

Donckt, J. V. D., Donckt, J. V. D., Deprost, E., & Hoecke, S. V. (2022) · SoftwareX · doi:10.1016/j.softx.2021.100971 · Cited by 19

Learning phenotypes and dynamic patient representations via RNN regularized collective non-negative tensor factorization

Yin, K., Qian, D., Cheung, W. K., Fung, B. C. M., & Poon, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011246 · Cited by 19

A Hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM)–Attention Model Architecture for Precise Medical Image Analysis and Disease Diagnosis

Hayat, M. T., Allawi, Y. M., Alamro, W., Sultan, S. M., Abadleh, A., Kang, H., & Zreikat, A. I. (2025) · Diagnostics · doi:10.3390/diagnostics15212673 · Cited by 18

KIT-LSTM: Knowledge-guided Time-aware LSTM for Continuous Clinical Risk Prediction

Liu, L. J., Ortiz-Soriano, V., Neyra, J. A., & Chen, J. (2022) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm55620.2022.9994931 · Cited by 21

Analysing an imbalanced stroke prediction dataset using machine learning techniques

Elangovan, V. S., Rajeswari, D., Khalaf, O. I., Sharif, M. S., & Elmedany, W. M. (2024) · Karbala International Journal of Modern Science · doi:10.33640/2405-609x.3355 · Cited by 18

The Transformative Impact of Big Data in Healthcare: Improving Outcomes, Safety, and Efficiencies

Mazumder, M. S. A. (2024) · GLOBAL MAINSTREAM JOURNAL · doi:10.62304/jbedpm.v3i03.82 · Cited by 18

Fuzzy rule based classifier model for evidence based clinical decision support systems

Navin, K. S., & B, M. K. M. (2024) · Intelligent Systems with Applications · doi:10.1016/j.iswa.2024.200393 · Cited by 24

A fusion framework to extract typical treatment patterns from electronic medical records

Chen, J., Sun, L., Guo, C., & Xie, Y. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101782 · Cited by 17

Merging Data Diversity of Clinical Medical Records to Improve Effectiveness

Helgheim, B. I., Maia, R., Ferreira, J. C., & Martins, A. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16050769 · Cited by 16

Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches, and Future Directions

Jia, Q., Liu, Y., Ren, S., & Zhu, K. Q. (2023) · ACM Computing Surveys · doi:10.1145/3622933 · Cited by 20

Using anchors from free text in electronic health records to diagnose postoperative delirium

Mikalsen, K. Ø., Soguero-Ruíz, C., Jensen, K., Hindberg, K., Gran, M., Revhaug, A., Lindsetmo, R., Skrøvseth, S. O., Godtliebsen, F., & Jenssen, R. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.09.014 · Cited by 16

Don't Do Imputation: Dealing with Informative Missing Values in EHR Data Analysis

Li, J., Wang, M., Steinbach, M. S., Kumar, V., & Simon, G. (2018) · IEEE International Conference on Big Knowledge (ICBK) · doi:10.1109/icbk.2018.00062 · Cited by 15

Mining incomplete clinical data for the early assessment of Kawasaki disease based on feature clustering and convolutional neural networks

Wang, H., Tan, X.-H., Huang, Z., Pan, B., & Tian, J. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101859 · Cited by 18

Network analysis of autistic disease comorbidities in Chinese children based on ICD-10 codes

Li, X., Liu, G., Chen, W.-X., Bi, Z., & Liang, H. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01282-z · Cited by 16

Artificial intelligence in drug discovery — what it is, where we stand and the path forward

Bender, A., Thomas, M. C., Scannell, J. W., Shaywitz, D. A., Ghiandoni, G. M., Greener, J. G., Pruteanu, L.-L., Jacobson, R. D., Handa, K., Hirano, M., et al. (2026) · Nature Reviews Drug Discovery · doi:10.1038/s41573-026-01496-2 · Cited by 27

Data structuring of electronic health records: a systematic review

de Oliveira, J. M., da Costa, C. A., & Antunes, R. S. (2021) · Health and Technology · doi:10.1007/s12553-021-00607-w · Cited by 19

Mining Electronic Health Records: A SurveyPreprint

Yadav, P., Steinbach, M. S., Kumar, V., & Simon, G. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.03222 · Cited by 20

The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges

Sarwar, T., Seifollahi, S., Chan, J., Zhang, X., Aksakallı, V., Hudson, I. L., Verspoor, K. M., & Cavedon, L. (2022) · ACM Computing Surveys · doi:10.1145/3490234 · Cited by 151

A Collective Study of Data Mining Techniques for the Big Health Data available from the Electronic Health Records

Pooja, H., & Jagadeesh, P. (2019) · International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE) · doi:10.1109/icatiece45860.2019.9063623 · Cited by 8

Text Mining to Support Knowledge Discovery from Electronic Health Records

Afzal, Z. (2018) · RePub (Erasmus University Rotterdam)

Data Mining Approaches on EHR System

Thilagavathy, R., Veeramani, T., Deebalakshmi, R., & Sundaravadivazhagan, B. (2023) · CRC Press eBooks · doi:10.1201/9781003309451-13