The diagnosis of dengue in patients presenting with acute febrile illness using supervised machine learning and impact of seasonality
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 pat...
Main Authors: | Ming, DK, Tuan, NM, Hernandez, B, Sangkaew, S, Vuong, NL, Chanh, HQ, Chau, NVV, Simmons, CP, Wills, B, Georgiou, P, Holmes, AH, Yacoub, S |
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מחברים אחרים: | Vietnam ICU Translational Applications Laboratory (VITAL) Investigators |
פורמט: | Journal article |
שפה: | English |
יצא לאור: |
Frontiers Media
2022
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פריטים דומים
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Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam
מאת: Ming, DK, et al.
יצא לאור: (2022) -
Higher plasma viremia in the febrile phase is associated with adverse dengue outcomes irrespective of infecting serotype or host immune status: an analysis of 5642 Vietnamese cases
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Continuous physiological monitoring using wearable technology to inform individual management of infectious diseases, public health and outbreak responses
מאת: Ming, DK, et al.
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Enhancing risk prediction of progression to severe disease during the febrile phase of dengue: a systematic review and meta-analysis
מאת: Sangkaew, S, et al.
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Combination of inflammatory and vascular markers in the febrile phase of dengue is associated with more severe outcomes
מאת: Vuong, NL, et al.
יצא לאור: (2021)