This paper is in the following e-collection/theme issue:

Machine Learning (1400) Sleep Apnea (23) Sleep Medicine (22) Clinical Information and Decision Making (1339) Decision Support for Health Professionals (1145) Clinical Informatics (918)

Published on in Vol 9, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25124, first published .
Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

Enhancing Obstructive Sleep Apnea Diagnosis With Screening Through Disease Phenotypes: Algorithm Development and Validation

Authors of this article:

Daniela Ferreira-Santos1, 2 Author Orcid Image ;   Pedro Pereira Rodrigues1, 2 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Ruz G, Henríquez P, Mascareño A. Bayesian Constitutionalization: Twitter Sentiment Analysis of the Chilean Constitutional Process through Bayesian Network Classifiers. Mathematics 2022;10(2):166 View
  2. de Araujo Dantas A, Gonçalves F, Martins A, Alves G, Stechman-Neto J, Corrêa C, Santos R, Nascimento W, de Araujo C, Taveira K. Worldwide prevalence and associated risk factors of obstructive sleep apnea: a meta-analysis and meta-regression. Sleep and Breathing 2023;27(6):2083 View
  3. Huang J, Zhuang J, Zheng H, Yao L, Chen Q, Wang J, Fan C. A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators. Nature and Science of Sleep 2024;Volume 16:413 View