Abstract
One in 25 deaths worldwide is related to liver disease, and often with multiple hepatosplenic conditions. Yet, little is understood of the risk factors for hepatosplenic multimorbidity, especially in the context of chronic infections. We present a novel Bayesian multitask learning framework to jointly model 45 hepatosplenic conditions assessed using point-of-care B-mode ultrasound for 3155 individuals aged 5-91 years within the SchistoTrack cohort across rural Uganda, where chronic intestinal schistosomiasis is endemic. We identify distinct and shared biomedical, socioeconomic, and spatial risk factors for individual conditions and hepatosplenic multimorbidity, and introduce methods for measuring condition dependencies as risk factors. Notably, for gastro-oesophageal varices, we discover key risk factors of older age, lower haemoglobin concentration, and schistosomal periportal fibrosis. Our findings provide a compendium of risk factors to inform surveillance, triage, and follow-up, while our model enables improved prediction of hepatosplenic multimorbidity, and if validated on other anatomical systems, general multimorbidity.
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Data availability
The raw data are protected and are not available due to data privacy laws. The metadata generated in this study are provided in the Supplementary Information.
Code availability
Synthetically generated data is provided based on random sampling of the covariates and conditions to allow the running of the code. The model implementation code is shared as supplementary material and can be found in Supplementary Code 1.
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Acknowledgements
We are thankful for the involvement of our study participants and the SchistoTrack teams, especially the surveyors, nurses, sonographers, and laboratory technicians. We also like to thank the Uganda Ministry of Health, local district leaders, focal health workers, and village health teams. Special thanks also to the Oxford team for the fieldwork, data wrangling, everyday discussions, and feedback. This research was funded in whole, or in part, by the UKRI EPSRC [EP/X021793/1]. For the purpose of Open Access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. NDPH Pump Priming Fund, John Fell Fund, Robertson Foundation, UKRI EPSRC (EP/X021793/1) grants were awarded to G.F.C.
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Conceptualisation: G.F.C. and Y.C.Z. Data curation: Y.C.Z., V.A., J.B.O., B.N., C.K.O., N.B.K. and G.F.C. Formal analysis: Y.C.Z. Investigation, methodology, visualisation: Y.C.Z. Writing - original draft: Y.C.Z. and G.F.C. Validation: Y.C.Z. and G.F.C. Writing - review and editing: Y.C.Z., V.A., J.B.O., B.N., C.K.O., N.B.K. and G.F.C. Funding acquisition and supervision: G.F.C. Resources: G.F.C.
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Zhi, YC., Anguajibi, V., Oryema, J.B. et al. Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69528-4
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DOI: https://doi.org/10.1038/s41467-026-69528-4
