Machine learning improves diagnostic performance for malaria in returned travelers, suggesting new clinical tools are needed.
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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Furuya-Kanamori et al. (2026) studied this question.
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