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Predicting adherence to continuous positive airway pressure in patients with obstructive sleep apnea syndrome through machine learning

2022-09Conference object Open access
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dc.contributor.authorPereira, M. A. Pacheco
dc.contributor.authorTeresa Ribeiro, Ricardo
dc.date.accessioned2023-08-28T10:51:44Z
dc.date.available2023-08-28T10:51:44Z
dc.date.issued2022-09
dc.description.abstractMany patients with Obstructive Sleep Apnea Syndrome (OSAS) require Continuous Positive Airway Pressure (CPAP) therapy. Despite its high efficacy, both in the short and long term, treatment through CPAP has low adherence rates, even with the technological advances in recent years. In this study, using machine learning algorithms, we tried to predict which patients would be successful in adhering to CPAP treatment (mean =4h per night), three months after the beginning of the treatment, through the data obtained from a multicentre public database (n=175). After comparing six algorithms, Neural Networks (NN) was the one that showed the best results, with an f1-score of 0.71 and an AUC of 0.75, followed by Linear Regression, kNN, SVM, Naive Bayes, and Random Forests. Ten relevant characteristics were also identified for predicting adherence success: severity of OSAS, time til treatment, waist perimeter, score of FOSQ global, Apnea-Hypopnea Index, seizure diagnostic, type of sleep study (home vs. full night in laboratory vs. split night in laboratory), liver disease diagnostic and score FOSQ vigilance. It is possible to conclude that ML algorithms, properly trained in Big Data systems, may have a reasonable predictive capacity for the success of patients' adherence to CPAP, thus allowing personalized therapy with an improvement in their quality of life.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationPereira MP, Ribeiro RT. Predicting adherence to continuous positive airway pressure in patients with obstructive sleep apnea syndrome through machine learning. In: ERS International Congress 2022, Barcelona (Spain), September 4-6, 2022. Eur Respir J. 2022;60(Suppl):4291.pt_PT
dc.identifier.doi10.1183/13993003.congress-2022.4291pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.21/16417
dc.language.isoporpt_PT
dc.peerreviewedyespt_PT
dc.publisherEuropean Respiratory Societypt_PT
dc.relation.publisherversionhttps://www.ers-education.org/lr/show-details/?idP=270371pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectObstructive sleep apnea syndromept_PT
dc.subjectContinuous positive airway pressurept_PT
dc.subjectPersonalized medicinept_PT
dc.titlePredicting adherence to continuous positive airway pressure in patients with obstructive sleep apnea syndrome through machine learningpt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.issueSupplpt_PT
oaire.citation.startPage4291pt_PT
oaire.citation.volume60pt_PT
person.familyNameTeresa Ribeiro
person.givenNameRicardo
person.identifier1186219
person.identifier.ciencia-id6510-F45E-24C3
person.identifier.orcid0000-0003-2769-9934
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication15f2b0d2-29ff-428e-8e1e-cde698e5d9a5
relation.isAuthorOfPublication.latestForDiscovery15f2b0d2-29ff-428e-8e1e-cde698e5d9a5

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