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

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

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 tuber...

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Bibliographic Details
Main Authors: Hooman H. Rashidi, Imran H. Khan, Luke T. Dang, Samer Albahra, Ujjwal Ratan, Nihir Chadderwala, Wilson To, Prathima Srinivas, Jeffery Wajda, Nam K. Tran
Format: Article
Language:English
Published: Elsevier 2022-01-01
Series:Journal of Pathology Informatics
Subjects:
Artificial intelligence
biomarkers
data accessibility
electronic medical record
privacy
simulation
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353922007660

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http://www.sciencedirect.com/science/article/pii/S2153353922007660

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