The diagnosis of dengue in patients presenting with acute febrile illness using supervised machine learning and impact of seasonality - Ming, DK; Tuan, NM; Hernandez, B; Sangkaew, S; Vuong, NL; Chanh, HQ; Chau, NVV; Simmons, CP; Wills, B; Georgiou, P; Holmes, AH; Yacoub, S (Frontiers Media, 2022)
Background: Symptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.