Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Journals

1. Datta S, Roberts K. Weakly supervised spatial relation extraction from radiology reports. JAMIA Open 2023;6(2) View
2. Sun M, Yang X, Niu J, Gu Y, Wang C, Zhang W. A cross-modal clinical prediction system for intensive care unit patient outcome. Knowledge-Based Systems 2024;283:111160 View
3. Guo Z. Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies. Journal of the American Statistical Association 2023:1 View
4. Jaiswal A, Katz A, Nesca M, Milios E. Identifying Risk Factors Associated With Lower Back Pain in Electronic Medical Record Free Text: Deep Learning Approach Using Clinical Note Annotations. JMIR Medical Informatics 2023;11:e45105 View
5. Kottahachchi Kankanamge Don A, Khalil I. Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework. ACM Transactions on Quantum Computing 2025;6(1):1 View
6. Maleki D, Rahnamayan S, Tizhoosh H. A self-supervised framework for cross-modal search in histopathology archives using scale harmonization. Scientific Reports 2024;14(1) View
7. van der Meijden S, van Boekel A, van Goor H, Nelissen R, Schoones J, Steyerberg E, Geerts B, de Boer M, Arbous M. Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review. JMIR Medical Informatics 2024;12:e57195 View
8. Cho H, Jun T, Kim Y, Kang H, Ahn I, Gwon H, Kim Y, Seo H, Choi H, Kim M, Han J, Kee G, Park S, Ko S. Task-Specific Transformer-Based Language Models in Health Care: A Scoping Review (Preprint). JMIR Medical Informatics 2023 View
9. Chopra S, Carroll J, Pater J. Providing Context to the "Unknown": Patient and Provider Reflections on Connecting Personal Tracking, Patient-Reported Insights, and EHR Data within a Post-COVID Clinic. Proceedings of the ACM on Human-Computer Interaction 2024;8(CSCW2):1 View
10. Xu X, Yun B, Zhao Y, Jin L, Zong Y, Yu G, Zhao C, Fan K, Zhang X, Tan S, Zhang Z, Wang Y, Li Q, Yu S. Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System. Bioengineering 2024;12(1):10 View
11. Rezk E, Eltorki M, El-Dakhakhni W. Human knowledge-based artificial intelligence methods for skin cancer management: Accuracy and interpretability study. Smart Health 2025;36:100540 View
12. Ren L, Belkadi S, Han L, Del-Pinto W, Nenadic G. Synthetic4Health: generating annotated synthetic clinical letters. Frontiers in Digital Health 2025;7 View
13. Yoon T, Kang D. Integrating snapshot ensemble learning into masked autoencoders for efficient self-supervised pretraining in medical imaging. Scientific Reports 2025;15(1) View
14. Ouaari S, Burak Ünal A, Akgün M, Pfeifer N. Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach. IEEE Access 2025;13:151527 View

Books/Policy Documents

1. Greatrix T, Langbein F, Whitaker R, Colombo G, Turner L. Artificial Intelligence in Healthcare. View

Conference Proceedings

1. Cho Y, Joshi G, Dimitriadis D. 2023 IEEE/CVF International Conference on Computer Vision (ICCV). Local or Global: Selective Knowledge Assimilation for Federated Learning with Limited Labels View
2. Tassi S, Polyzos K, Fotiadis D, Sakellarios A. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Gaussian Process-based Active Learning for Efficient Cardiovascular Disease Inference View
3. Perera J, Rajapaksha U, Premadasa G, Weerasinghe C, Methmini H, Nethusara S. 2023 5th International Conference on Advancements in Computing (ICAC). “DiagnoMe” Mobile Application for Identifying and Predicting the Chronic Diseases View
4. Feng Y, Zhang Y, Shang Y. 2024 IEEE Intelligent Mobile Computing (MobileCloud). Toward Optimal Amount of Training Annotations for Waterfowl Detection and Classification View