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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/138980
Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
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More than 400,000 deaths from severe malaria (SM) are
reported every year, mainly in African children. The diversity
of clinical presentations associated with SM indicates important
differences in disease pathogenesis that require specific
treatment, and this clinical heterogeneity of SM remains poorly
understood. Here, we apply tools from machine learning and
model-based inference to harness large-scale data and dissect
the heterogeneity in patterns of clinical features associated
with SM in 2904 Gambian children admitted to hospital with
malaria. This quantitative analysis reveals features predicting
the severity of individual patient outcomes, and the dynamic
pathways of SM progression, notably inferred without requiring
longitudinal observations. Bayesian inference of these pathways
allows us assign quantitative mortality risks to individual
patients. By independently surveying expert practitioners, we
show that this data-driven approach agrees with and expands the
current state of knowledge on malaria progression, while
simultaneously providing a data-supported framework for
predicting clinical risk.
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JOHNSTON, Iain G., et al. Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data. NPJ Digital Medicine. 2019. Vol. 2. ISSN 2398-6352. [consulted: 15 of October of 2026]. Available at: https://hdl.handle.net/2445/138980