Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review
BackgroundAmerican Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard. ObjectiveWe aimed to identify, gather, and a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-09-01
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| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2022/9/e39452 |
| _version_ | 1797734707747618816 |
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| author | Daniela Ferreira-Santos Pedro Amorim Tiago Silva Martins Matilde Monteiro-Soares Pedro Pereira Rodrigues |
| author_facet | Daniela Ferreira-Santos Pedro Amorim Tiago Silva Martins Matilde Monteiro-Soares Pedro Pereira Rodrigues |
| author_sort | Daniela Ferreira-Santos |
| collection | DOAJ |
| description |
BackgroundAmerican Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard.
ObjectiveWe aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA.
MethodsWe searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study.
ResultsOur search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression.
ConclusionsAlthough high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition.
Trial RegistrationPROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339 |
| first_indexed | 2024-03-12T12:47:15Z |
| format | Article |
| id | doaj.art-4321911af2114210a2413805ee2b81db |
| institution | Directory Open Access Journal |
| issn | 1438-8871 |
| language | English |
| last_indexed | 2024-03-12T12:47:15Z |
| publishDate | 2022-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj.art-4321911af2114210a2413805ee2b81db2023-08-28T23:09:44ZengJMIR PublicationsJournal of Medical Internet Research1438-88712022-09-01249e3945210.2196/39452Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic ReviewDaniela Ferreira-Santoshttps://orcid.org/0000-0002-0390-9944Pedro Amorimhttps://orcid.org/0000-0001-7466-4174Tiago Silva Martinshttps://orcid.org/0000-0002-2718-7093Matilde Monteiro-Soareshttps://orcid.org/0000-0002-4586-2910Pedro Pereira Rodrigueshttps://orcid.org/0000-0001-7867-6682 BackgroundAmerican Academy of Sleep Medicine guidelines suggest that clinical prediction algorithms can be used to screen patients with obstructive sleep apnea (OSA) without replacing polysomnography, the gold standard. ObjectiveWe aimed to identify, gather, and analyze existing machine learning approaches that are being used for disease screening in adult patients with suspected OSA. MethodsWe searched the MEDLINE, Scopus, and ISI Web of Knowledge databases to evaluate the validity of different machine learning techniques, with polysomnography as the gold standard outcome measure and used the Prediction Model Risk of Bias Assessment Tool (Kleijnen Systematic Reviews Ltd) to assess risk of bias and applicability of each included study. ResultsOur search retrieved 5479 articles, of which 63 (1.15%) articles were included. We found 23 studies performing diagnostic model development alone, 26 with added internal validation, and 14 applying the clinical prediction algorithm to an independent sample (although not all reporting the most common discrimination metrics, sensitivity or specificity). Logistic regression was applied in 35 studies, linear regression in 16, support vector machine in 9, neural networks in 8, decision trees in 6, and Bayesian networks in 4. Random forest, discriminant analysis, classification and regression tree, and nomogram were each performed in 2 studies, whereas Pearson correlation, adaptive neuro-fuzzy inference system, artificial immune recognition system, genetic algorithm, supersparse linear integer models, and k-nearest neighbors algorithm were each performed in 1 study. The best area under the receiver operating curve was 0.98 (0.96-0.99) for age, waist circumference, Epworth Somnolence Scale score, and oxygen saturation as predictors in a logistic regression. ConclusionsAlthough high values were obtained, they still lacked external validation results in large cohorts and a standard OSA criteria definition. Trial RegistrationPROSPERO CRD42021221339; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=221339https://www.jmir.org/2022/9/e39452 |
| spellingShingle | Daniela Ferreira-Santos Pedro Amorim Tiago Silva Martins Matilde Monteiro-Soares Pedro Pereira Rodrigues Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review Journal of Medical Internet Research |
| title | Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review |
| title_full | Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review |
| title_fullStr | Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review |
| title_full_unstemmed | Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review |
| title_short | Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review |
| title_sort | enabling early obstructive sleep apnea diagnosis with machine learning systematic review |
| url | https://www.jmir.org/2022/9/e39452 |
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