Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical studyResearch in context

Summary: Background: Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) uti...

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Detaylı Bibliyografya
Asıl Yazarlar:	Stefan Karolcik, Vasileos Manginas, Ho Quang Chanh, John Daniels, Nguyen Thi Giang, Vu Ngo Thanh Huyen, Minh Tu Van Hoang, Khanh Phan Nguyen Quoc, Bernard Hernandez, Damien K. Ming, Hao Nguyen Van, Tu Qui Phan, Huynh Trung Trieu, Tai Luong Thi Hue, Alison H. Holmes, Louise Thwaites, Tho Phan Vinh, Sophie Yacoub, Pantelis Georgiou
Materyal Türü:	Artigo
Dil:	Inglês
Baskı/Yayın Bilgisi:	Elsevier 2024-06-01
Seri Bilgileri:	EBioMedicine
Konular:	Dengue Photoplethysmography (PPG) Deep learning
Online Erişim:	http://www.sciencedirect.com/science/article/pii/S2352396424001993
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Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical studyResearch in context

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