# name
Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data

# metadata.dc.description.abstract[].value
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.

# metadata.dc.source[].value
Articles publicats en revistes (ISGlobal)

# metadata.dc.title[].value
Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data