This paper is in the following e-collection/theme issue:

Letters (85) Machine Learning (1465) Electronic Health Records (1053)

Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/10969, first published .
Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

Data Leakage in Health Outcomes Prediction With Machine Learning. Comment on “Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning”

Authors of this article:

Alexandre Chiavegatto Filho1 Author Orcid Image ;   André Filipe De Moraes Batista1 Author Orcid Image ;   Hellen Geremias dos Santos1 Author Orcid Image
Article Authors Cited by (13) Tweetations (19) Metrics

Journals

  1. Mamidi T, Tran-Nguyen T, Melvin R, Worthey E. Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data. Frontiers in Big Data 2021;4 View
  2. McElhinney J, Catacutan M, Mawart A, Hasan A, Dias J. Interfacing Machine Learning and Microbial Omics: A Promising Means to Address Environmental Challenges. Frontiers in Microbiology 2022;13 View
  3. Zhou Y, Koyuncu C, Lu C, Grobholz R, Katz I, Madabhushi A, Janowczyk A. Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer. Medical Image Analysis 2023;84:102702 View
  4. Reimer T, Pistorius S. Review and Analysis of Tumour Detection and Image Quality Analysis in Experimental Breast Microwave Sensing. Sensors 2023;23(11):5123 View
  5. Das S, Bhuyan R, Baro B, Das U, Sharma R, Bayan S. Flexible triboelectric nanogenerators of Au-g-C3N4/ZnO hierarchical nanostructures for machine learning enabled body movement detection. Nanotechnology 2023;34(44):445501 View
  6. Davis S, Matheny M, Balu S, Sendak M. A framework for understanding label leakage in machine learning for health care. Journal of the American Medical Informatics Association 2023;31(1):274 View
  7. Zinnel L, Bentil S. Convolutional neural networks for traumatic brain injury classification and outcome prediction. Health Sciences Review 2023;9:100126 View
  8. Huang A, Huang S. Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension. The Journal of Clinical Hypertension 2023;25(12):1135 View
  9. Kapoor S, Cantrell E, Peng K, Pham T, Bail C, Gundersen O, Hofman J, Hullman J, Lones M, Malik M, Nanayakkara P, Poldrack R, Raji I, Roberts M, Salganik M, Serra-Garcia M, Stewart B, Vandewiele G, Narayanan A. REFORMS: Consensus-based Recommendations for Machine-learning-based Science. Science Advances 2024;10(18) View
  10. Nolin-Lapalme A, Corbin D, Tastet O, Avram R, Hussin J. Advancing Fairness in Cardiac Care: Strategies for Mitigating Bias in Artificial Intelligence Models Within Cardiology. Canadian Journal of Cardiology 2024;40(10):1907 View
  11. Varga D. Critical Analysis of Data Leakage in WiFi CSI-Based Human Action Recognition Using CNNs. Sensors 2024;24(10):3159 View
  12. Bernett J, Blumenthal D, Grimm D, Haselbeck F, Joeres R, Kalinina O, List M. Guiding questions to avoid data leakage in biological machine learning applications. Nature Methods 2024;21(8):1444 View
  13. Hosseiniyan Khatibi S, Dimaano N, Veliz E, Sundaresan V, Ali J. Exploring and exploiting the rice phytobiome to tackle climate change challenges. Plant Communications 2024:101078 View