Unsupervised Phenotyping to Stratify Surgical Outcomes in Obstructive Sleep Apnea.

Key Points

• This research aims to identify phenotypes of obstructive sleep apnea and their associated surgical outcomes through unsupervised clustering.
• Utilized unsupervised clustering techniques on multidimensional data related to obstructive sleep apnea.
• Identified distinct phenotypes associated with differing surgical prognoses.
• Integrated findings into preoperative evaluation processes.
• Revealed multiple distinct obstructive sleep apnea phenotypes.
• Each phenotype correlated with different surgical outcomes.
• Enhanced preoperative counseling and decision-making approaches.