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Misdiagnosis of enteric fever is a major global health problem, resulting in patient mismanagement, antimicrobial misuse and inaccurate disease burden estimates. Applying a machine learning algorithm to host gene expression profiles, we identified a diagnostic signature, which could distinguish culture-confirmed enteric fever cases from other febrile illnesses (area under receiver operating characteristic curve > 95%). Applying this signature to a culture-negative suspected enteric fever cohort in Nepal identified a further 12.6% as likely true cases. Our analysis highlights the power of data-driven approaches to identify host response patterns for the diagnosis of febrile illnesses. Expression signatures were validated using qPCR, highlighting their utility as PCR-based diagnostics for use in endemic settings.

Original publication




Journal article


EMBO Mol Med

Publication Date





biomarker, enteric fever, machine learning, transcriptomics, Diagnosis, Differential, Gene Expression Profiling, Humans, Machine Learning, Molecular Diagnostic Techniques, Nepal, Polymerase Chain Reaction, ROC Curve, Typhoid Fever