Academic Journal
Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach
| العنوان: | Identifying Elevated Risk for Future Pain Crises in Sickle-Cell Disease Using Photoplethysmogram Patterns Measured During Sleep: A Machine Learning Approach |
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| المؤلفون: | Yunhua Ji, Patjanaporn Chalacheva, Carol L. Rosen, Michael R. DeBaun, Thomas D. Coates, Michael C. K. Khoo |
| المصدر: | Frontiers in Digital Health, Vol 3 (2021) |
| بيانات النشر: | Frontiers Media S.A., 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Medicine LCC:Public aspects of medicine LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | sickle cell anemia, photoplethysmography, peripheral vasoconstriction, sleep, machine learning, vaso-occlusive crises, Medicine, Public aspects of medicine, RA1-1270, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | Transient increases in peripheral vasoconstriction frequently occur in obstructive sleep apnea and periodic leg movement disorder, both of which are common in sickle cell disease (SCD). These events reduce microvascular blood flow and increase the likelihood of triggering painful vaso-occlusive crises (VOC) that are the hallmark of SCD. We recently reported a significant association between the magnitude of vasoconstriction, inferred from the finger photoplethysmogram (PPG) during sleep, and the frequency of future VOC in 212 children with SCD. In this study, we present an improved predictive model of VOC frequency by employing a two-level stacking machine learning (ML) model that incorporates detailed features extracted from the PPG signals in the same database. The first level contains seven different base ML algorithms predicting each subject's pain category based on the input PPG characteristics and other clinical information, while the second level is a meta model which uses the inputs to the first-level model along with the outputs of the base models to produce the final prediction. Model performance in predicting future VOC was significantly higher than in predicting VOC prior to each sleep study (F1-score of 0.43 vs. 0.35, p-value |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2673-253X |
| Relation: | https://www.frontiersin.org/articles/10.3389/fdgth.2021.714741/full; https://doaj.org/toc/2673-253X |
| DOI: | 10.3389/fdgth.2021.714741 |
| URL الوصول: | https://doaj.org/article/500ce4fcb80e40a69f8d56efa08b9cc1 |
| رقم الانضمام: | edsdoj.500ce4fcb80e40a69f8d56efa08b9cc1 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2673253X |
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| DOI: | 10.3389/fdgth.2021.714741 |