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

• Main Authors: Hooman H Rashidi (Author), Imran H Khan (Author), Luke T Dang (Author), Samer Albahra (Author), Ujjwal Ratan (Author), Nihir Chadderwala (Author), Wilson To (Author), Prathima Srinivas (Author), Jeffery Wajda (Author), Nam K Tran (Author)
• Format: Book
• Published: Elsevier, 2022-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2153-3539; 10.4103/jpi.jpi_75_21
• Keywords: artificial intelligence; biomarkers; data accessibility; electronic medical record; privacy; simulation; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 13, Iss 1, Pp 10-10 (2022)

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.