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Fig. 3 | Genome Medicine

Fig. 3

From: An 8-gene machine learning model improves clinical prediction of severe dengue progression

Fig. 3

The locked 8-gene XGBoost model predicts progression to SD in an independent prospective dengue cohort. A Description of independent Colombia cohort. Blood samples were collected upon presentation from dengue patients presenting with or without warning signs. B Confusion matrix depicting the number of patients with an initial diagnosis of D or DWS upon presentation and final diagnosis of D, DWS, or SD. C ROC curve of the locked 8-gene XGBoost model in predicting progression to SD in the independent cohort. The black point indicates the sensitivity and specificity of the 8-gene model at the Youden threshold in the independent cohort. The red point indicates the sensitivity and specificity of clinical warning signs in predicting progression to SD in the independent cohort. D 8-gene model predictions on samples collected throughout the disease course, on days 0–3, 4–6, or 7–10 post-fever onset. E Violin plot of the predicted probabilities of progression to SD for SD progressors in the independent cohort who initially presented with or without warning signs. F Predicted probabilities using the 8-gene model for the 22 patients in the independent Colombia cohort who progressed to SD, by days from sample collection to the appearance of severe manifestations (“Days to SD Onset”). “0” indicates patients whose sample was collected on the day of—but at least several hours prior to—the appearance of SD manifestations. The dotted horizontal line indicates the Youden threshold in the Colombia cohort

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