Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples.

ABSTRACT:

Background

Prediction tools without patient-reported symptoms could facilitate widespread identification of OSA.

Research question

What is the diagnostic performance of OSA prediction tools derived from machine learning using readily available data without patient responses to questionnaires? Also, how do they compare with STOP-BANG, an OSA prediction tool, in clinical and community-based samples?

Study design and methods

Logistic regression and machine learning techniques, including artificial neural network (ANN), random forests (RF), and kernel support vector machine, were used to determine the ability of age, sex, BMI, and race to predict OSA status. A retrospective cohort of 17,448 subjects from sleep clinics within the international Sleep Apnea Global Interdisci

SUBMITTER: Holfinger SJ 

PROVIDER: S-EPMC8941600 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature