Journal of Pathology Informatics

RESEARCH ARTICLE
Year
: 2022  |  Volume : 13  |  Issue : 1  |  Page : 10-

Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data


Hooman H Rashidi1, Imran H Khan1, Luke T Dang1, Samer Albahra1, Ujjwal Ratan2, Nihir Chadderwala2, Wilson To2, Prathima Srinivas2, Jeffery Wajda3, Nam K Tran1 
1 Department of Pathology and Laboratory Medicine, University of California, Davis, School of Medicine, Sacramento, California, United States of America
2 Amazon Web Services, Seattle, Washington, United States of America
3 UC Davis Health, Sacramento, California, United States of America

Correspondence Address:
Dr. Hooman H Rashidi
Dept. of Pathology and Laboratory Medicine, University of California Davis, 4400 V St., Sacramento 95817.
United States of America

High-quality medical data is critical to the development and implementation of machine learning (ML) algorithms in healthcare; however, security, and privacy concerns continue to limit access. We sought to determine the utility of “synthetic data” in training ML algorithms for the detection of tuberculosis (TB) from inflammatory biomarker profiles. A retrospective dataset (A) comprised of 278 patients was used to generate synthetic datasets (B, C, and D) for training models prior to secondary validation on a generalization dataset. ML models trained and validated on the Dataset A (real) demonstrated an accuracy of 90%, a sensitivity of 89% (95% CI, 83–94%), and a specificity of 100% (95% CI, 81–100%). Models trained using the optimal synthetic dataset B showed an accuracy of 91%, a sensitivity of 93% (95% CI, 87–96%), and a specificity of 77% (95% CI, 50–93%). Synthetic datasets C and D displayed diminished performance measures (respective accuracies of 71% and 54%). This pilot study highlights the promise of synthetic data as an expedited means for ML algorithm development.


How to cite this article:
Rashidi HH, Khan IH, Dang LT, Albahra S, Ratan U, Chadderwala N, To W, Srinivas P, Wajda J, Tran NK. Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data.J Pathol Inform 2022;13:10-10


How to cite this URL:
Rashidi HH, Khan IH, Dang LT, Albahra S, Ratan U, Chadderwala N, To W, Srinivas P, Wajda J, Tran NK. Prediction of tuberculosis using an automated machine learning platform for models trained on synthetic data. J Pathol Inform [serial online] 2022 [cited 2022 May 21 ];13:10-10
Available from: https://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2022;volume=13;issue=1;spage=10;epage=10;aulast=Rashidi;type=0