STREAMLINE: An Automated Machine Learning Pipeline for Biomedicine Applied to Examine the Utility of Photography-Based Phenotypes for OSA Prediction Across International Sleep Centers

1 code implementation • 9 Dec 2023 • Ryan J. Urbanowicz, Harsh Bandhey, Brendan T. Keenan, Greg Maislin, Sy Hwang, Danielle L. Mowery, Shannon M. Lynch, Diego R. Mazzotti, Fang Han, Qing Yun Li, Thomas Penzel, Sergio Tufik, Lia Bittencourt, Thorarinn Gislason, Philip de Chazal, Bhajan Singh, Nigel McArdle, Ning-Hung Chen, Allan Pack, Richard J. Schwab, Peter A. Cistulli, Ulysses J. Magalang

While machine learning (ML) includes a valuable array of tools for analyzing biomedical data, significant time and expertise is required to assemble effective, rigorous, and unbiased pipelines.

Anatomy AutoML +2