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Related Experiment Video

Updated: Jul 16, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

Explainable Machine-Learning Model for Predicting Severe Obstructive Sleep Apnea in Patients Undergoing Metabolic

Wenhui Chen1, Lili Li2, Junsen Peng3

  • 1Department of Bariatric Surgery Center, First Affiliated Hospital of Jinan University, Guangzhou, China. jnucwh2019@163.com.

Obesity Surgery
|July 14, 2026
PubMed
Summary

Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...

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Validation Performance of Screening Tools for Predicting Obstructive Sleep Apnea Among Chinese Patients Undergoing Metabolic Bariatric Surgery: Insights from a Multicenter Database.

Obesity surgery·2026

A new machine learning model accurately predicts severe obstructive sleep apnea (SOSA) in bariatric surgery patients. This tool aids in risk stratification and personalized care, improving patient outcomes.

Area of Science:

  • Medical Informatics
  • Sleep Medicine
  • Bariatric Surgery

Background:

  • Severe obstructive sleep apnea (SOSA) increases perioperative risks in bariatric surgery patients.
  • Current polysomnography screening is costly and inaccessible.
  • Need for accessible screening methods for SOSA in bariatric surgery candidates.

Purpose of the Study:

  • Develop and validate an explainable machine learning (ML) model for SOSA prediction.
  • Identify key clinical variables for SOSA risk assessment.
  • Create a tool for perioperative risk stratification in bariatric surgery.

Main Methods:

  • Utilized data from 1,690 patients in the Chinese Obesity and Metabolic Surgery Database.
  • Employed feature selection techniques and evaluated eight ML algorithms.
Keywords:
Machine learningMetabolic bariatric surgeryObesitySevere obstructive sleep apneaShapley Additive Explanations (SHAP)

Related Experiment Videos

Last Updated: Jul 16, 2026

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea
07:54

Drug-Induced Sleep Endoscopy (DISE) with Target Controlled Infusion (TCI) and Bispectral Analysis in Obstructive Sleep Apnea

Published on: December 6, 2016

  • Assessed model interpretability using SHapley Additive exPlanations (SHAP).

    • Main Results:

    • Identified a random forest model with nine key predictors (e.g., neck circumference, BMI, age).
    • Achieved an AUC of 0.931 (training) and 0.869 (validation) for SOSA prediction.
    • The model was deployed as an accessible online tool.

    Conclusions:

    • An explainable random forest model effectively predicts SOSA in bariatric surgery patients.
    • The model offers clinical applicability for risk stratification and decision-making.
    • The free online tool facilitates targeted screening and cost-effective care.