Investigation on factors related to poor CPAP adherence using machine learning: a pilot study.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
15 11 2022
15 11 2022
Historique:
received:
18
02
2022
accepted:
06
10
2022
entrez:
15
11
2022
pubmed:
16
11
2022
medline:
19
11
2022
Statut:
epublish
Résumé
To improve patients' adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We developed a CPAP adherence prediction model using logistic regression and learn-to-rank machine learning with a pairwise approach. We then investigated adherence prediction performance targeting a 12-week period and the top ten factors correlating to poor CPAP adherence. The CPAP logs of 219 patients with obstructive sleep apnea (OSA) obtained from clinical treatment at Kyoto University Hospital were used. The highest adherence prediction accuracy obtained was an F1 score of 0.864. Out of the top ten factors obtained with the highest prediction accuracy, four were consistent with already-known clinical knowledge. The factors for better CPAP adherence indicate that air leakage should be avoided, mask pressure should be kept constant, and CPAP usage duration should be longer and kept constant. The results indicate that machine learning is an adequate method for investigating factors related to poor CPAP adherence.
Identifiants
pubmed: 36380059
doi: 10.1038/s41598-022-21932-8
pii: 10.1038/s41598-022-21932-8
pmc: PMC9666632
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
19563Subventions
Organisme : Japan Agency for Medical Research and Development
ID : JPEK0210116
Organisme : Ministry of Education, Culture, Sports, Science and Technology in Japan
ID : 17H04182
Organisme : Ministry of Health, Labor and Welfare in Japan
ID : 21FA1004
Organisme : Japan Agency for Medical Research and Development
ID : JPEK0210150
Organisme : Japan Agency for Medical Research and Development
ID : JPWM0425018
Organisme : JSPS KAKENHI
ID : 20H03690
Informations de copyright
© 2022. The Author(s).
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