Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/32903, first published .
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

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

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. Don A, Khalil I. Q-SupCon: Quantum-Enhanced Supervised Contrastive Learning Architecture within the Representation Learning Framework. ACM Transactions on Quantum Computing 2024 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