Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
Autor: | Climent Casals-Pascual, Mauricio Barahona, Nick S. Jones, Iain G. Johnston, Dominic P. Kwiatkowski, Muminatou Jallow, Ornella Cominetti, Till Hoffmann, Sam F. Greenbury |
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Přispěvatelé: | Engineering & Physical Science Research Council (EPSRC) |
Jazyk: | angličtina |
Rok vydání: | 2019 |
Předmět: |
medicine.medical_specialty
Longitudinal data Malària Medicine (miscellaneous) Inference Health Informatics Bayesian inference lcsh:Computer applications to medicine. Medical informatics Article 03 medical and health sciences 0302 clinical medicine Health Information Management Medicine Severe Malaria 030212 general & internal medicine Intensive care medicine Children 030304 developmental biology Developing world 0303 health sciences business.industry Applied mathematics medicine.disease Phenotype Malaria 3. Good health Computer Science Applications lcsh:R858-859.7 Identification (biology) business Infants |
Zdroj: | npj Digital Medicine, Vol 2, Iss 1, Pp 1-9 (2019) npj Digital Medicine Recercat. Dipósit de la Recerca de Catalunya instname NPJ Digital Medicine Dipòsit Digital de la UB Universidad de Barcelona |
ISSN: | 2398-6352 |
Popis: | 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. |
Databáze: | OpenAIRE |
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