Publications by authors named "Vincent Mzazi"
Background: Despite the adverse health outcomes associated with longer duration diarrhea (LDD), there are currently no clinical decision tools for timely identification and better management of children with increased risk. This study utilizes machine learning (ML) to derive and validate a predictive model for LDD among children presenting with diarrhea to health facilities.
Methods: LDD was defined as a diarrhea episode lasting ≥ 7 days.
Article Synopsis
- * A machine learning approach was applied using data from the VIDA and EFGH-Shigella studies in rural Kenya to create predictive models for LGF among children aged 6-35 months, encompassing 65 potential predictors including demographic and health-related factors.
- * The models showed a prevalence of LGF at 16.9% and 22.4% in different cohorts, with the gradient boosting model providing the best prediction accuracy, demonstrating its usefulness in identifying at-risk children for targeted healthcare interventions
Introduction: Diarrhea is still a significant global public health problem. There are currently no systematic evaluation of the modeling areas and approaches to predict diarrheal illness outcomes. This paper reviews existing research efforts in predictive modeling of infectious diarrheal illness in pediatric populations.
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