| Autor: |
Damien K. Ming, Nguyen M. Tuan, Bernard Hernandez, Sorawat Sangkaew, Nguyen L. Vuong, Ho Q. Chanh, Nguyen V. V. Chau, Cameron P. Simmons, Bridget Wills, Pantelis Georgiou, Alison H. Holmes, Sophie Yacoub |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Frontiers in Digital Health, Vol 4 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2673-253X |
| DOI: |
10.3389/fdgth.2022.849641 |
| Popis: |
BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of 90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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