Machine learning for scalable obstructive sleep apnea risk screening using digital phenotyping from wearable devices and clinical scales.

Abstract excerpt

Obstructive sleep apnea (OSA) is common yet frequently underdiagnosed, partly because overnight polysomnography (PSG) is logistically burdensome and access to specialized testing is limited. We aimed to develop machine-learning models for OSA risk screening using multimodal digital phenotyping from consumer-grade wearable devices, smartphone-based assessments, and clinical scales. We enrolled 338 participants and...

Topics

• Humans
• Sleep Apnea, Obstructive
• Machine Learning
• Wearable Electronic Devices
• Female
• Male