SLEEP 2026 | A novel OSA prediction model using machine learning

Nathanael Hwang, Beth Israel Deaconess Medical Center, Boston, MA, discusses the development of a machine learning model designed to improve screening for obstructive sleep apnea (OSA) using routinely collected electronic health record data. He highlights the performance of both comprehensive and simplified models, their validation against existing tools such as STOP-BANG, and the potential for automated, scalable OSA detection in clinical practice. This interview took place at the 40th annual meeting of the Associated Professional Sleep Societies (APSS) congress in Baltimore, MD.

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