Bayesian machine learning enables discovery of risk factors for hepatosplenic multimorbidity related to schistosomiasis

Journal article

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 identified distinct and shared biomedical, socioeconomic, and spatial risk factors for individual conditions and hepatosplenic multimorbidity, and introduced methods for measuring condition dependencies as risk factors. Notably, for gastro-oesophageal varices, we discovered key risk factors of older age, lower hemoglobin 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.

Authors: Zhi, Y-C; Anguajibi, V; Oryema, JB; Nabatte, B; Opio, CK

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature
• Journal: Nature Communications
• Publication date: 2026-03-03
• Acceptance date: 2026-01-23
• DOI: 10.1038/s41467-026-69528-4
• EISSN: 2041-1723
• Language: English
• Keywords: multimorbidity; Bayesian; machine learning; multitask learning; graphs; networks; schistosomiasis; schistosome; mansoni; severe morbidity; portal hypertension; hepatosplenic; intestinal; varices; UGIB; hematemesis; health state; ultrasound; sonography