Prediction of Tuberculosis Using an Automated Machine Learning Platform for Models Trained on Synthetic Data

Hooman H Rashidi; Imran H Khan; Luke T Dang; Samer Albahra; Ujjwal Ratan; Nihir Chadderwala; Wilson To; Prathima Srinivas; Jeffery Wajda; Nam K Tran

doi:10.4103/jpi.jpi_75_21

Prediction of Tuberculosis Using an Automated Machine Learning Platform for Models Trained on Synthetic Data

J Pathol Inform. 2022 Jan 20:13:10. doi: 10.4103/jpi.jpi_75_21. eCollection 2022.

Authors

Affiliations

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.

Abstract

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.

Keywords: Artificial intelligence; biomarkers; data accessibility; electronic medical record; privacy; simulation.