PediatricDatasetMadjouCM

This study uses a structured pediatric bronchiolitis dataset comprising 213 patient records collected from clinical settings. The dataset integrates multidimensional clinical information to support severity assessment and outcome prediction in hospitalized infants. Variables are organized into five categories. Demographic and administrative data include patient identifiers, recruitment sites (BRH and LRH), age in months, birth parameters, and hospitalization timelines. Clinical symptoms and anthropometric measurements capture respiratory manifestations (cough, rhinorrhea, respiratory distress), systemic symptoms (fever, vomiting, diarrhea), and admission metrics such as weight, height, and mid-upper arm circumference (MUAC). Vital signs and respiratory severity scores document heart rate, respiratory rate, oxygen saturation, and age-adapted indices (Silverman–Anderson for infants under three months and Bierman–Pierson for older children). Comorbidities and diagnostic investigations record underlying conditions (e.g., congenital heart disease, malnutrition, HIV/AIDS) and laboratory findings, including complete blood count parameters. Treatment and outcome variables include diagnostic procedures, therapeutic interventions (oxygen therapy, intravenous hydration), and discharge status (alive, deceased, or referred).