Beyond pre-travel risk assessment: machine learning for clinical diagnosis in returned travellers
Luis Furuya-Kanamori1, Alec Henderson1, Greta Vos2
1UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences, The University of Queensland, Herston, QLD, Australia.
Journal of Travel Medicine
|February 24, 2026
Abstract:
Machine learning (ML) applications in post-travel clinical care remain limited. ML-based models show good diagnostic performance for malaria when clinical, laboratory and travel itinerary data are incorporated, but only modest performance for gastrointestinal illness. Further work is needed to develop practical, clinically integrated ML-based decision-support tools for post-travel clinical care.
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