دورية أكاديمية
Investigation on factors related to poor CPAP adherence using machine learning: a pilot study
| العنوان: | Investigation on factors related to poor CPAP adherence using machine learning: a pilot study |
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| المؤلفون: | Kana Eguchi, Tsutomu Yabuuchi, Masayuki Nambu, Hirofumi Takeyama, Shozo Azuma, Kazuo Chin, Tomohiro Kuroda |
| المصدر: | Scientific Reports, Vol 12, Iss 1, Pp 1-9 (2022) |
| بيانات النشر: | Nature Portfolio, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Medicine LCC:Science |
| مصطلحات موضوعية: | Medicine, Science |
| الوصف: | Abstract 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2045-2322 |
| Relation: | https://doaj.org/toc/2045-2322 |
| DOI: | 10.1038/s41598-022-21932-8 |
| URL الوصول: | https://doaj.org/article/9da32ceadfc54b7288f33009b1f6d1cd |
| رقم الأكسشن: | edsdoj.9da32ceadfc54b7288f33009b1f6d1cd |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 20452322 |
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| DOI: | 10.1038/s41598-022-21932-8 |