Beyond pre-travel risk assessment: machine learning for clinical diagnosis in returned travellers

Key Points

• This research aims to evaluate the effectiveness of machine learning in clinical diagnosis of illnesses in returned travelers.
• Assessment of machine learning models for diagnosing malaria and gastrointestinal illnesses.
• Incorporation of clinical, laboratory, and travel itinerary data into models.
• Evaluation of diagnostic performance compared to traditional assessment methods.
• ML models show good performance for diagnosing malaria with integrated data.
• Modest performance observed for gastrointestinal illness diagnostics.
• Indicates a need for developing integrated decision-support tools in clinical settings.