ArticleNPJ digital medicine2019
Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.
Article in NPJ digital medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Quantifying uncertainty of predictions from cancer progression models.Bioinformatics (Oxford, England) · 2026Article
- Review
- Smartphone conjunctiva photography for malaria risk stratification in asymptomatic school age children.NPJ digital medicine · 2025Article
- A hypercubic Mk model framework for capturing reversibility in disease, cancer, and evolutionary accumulation modelling.Bioinformatics (Oxford, England) · 2024Article
- HyperTraPS-CT: Inference and prediction for accumulation pathways with flexible data and model structures.PLoS computational biology · 2024Article
- Source Attribution of Antibiotic Resistance Genes in Estuarine Aquaculture: A Machine Learning Approach.Antibiotics (Basel, Switzerland) · 2024Article
- HyperHMM: efficient inference of evolutionary and progressive dynamics on hypercubic transition graphs.Bioinformatics (Oxford, England) · 2023Article
- Understanding learner behaviour in online courses with Bayesian modelling and time series characterisation.Scientific reports · 2021Article
- Artificial intelligence and the future of global health.Lancet (London, England) · 2020Review
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Authors and funding
9 authors.
Funding
Abstract
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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Registered trials
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