Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study.

ABSTRACT:

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) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam.

Methods

We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified bet

SUBMITTER: Karolcik S 

PROVIDER: S-EPMC11167237 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature