The diagnosis of dengue in patients presenting with acute febrile illness using supervised machine learning and impact of seasonality
Background: Symptomatic 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 pat...
| Huvudupphovsmän: | , , , , , , , , , , , |
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| Övriga upphovsmän: | |
| Materialtyp: | Journal article |
| Språk: | English |
| Publicerad: |
Frontiers Media
2022
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