2026-10-13T22:55:13Z https://www.scidb.cn/oai

10.57760/sciencedb.28536 2025-09-27T01:35:33Z

2025-09-27 A machine learning model combining fungal biomarkers and routine laboratory indicators shows promise for improving early diagnosis of invasive fungal infections in critically ill children. doi:10.57760/sciencedb.28536 en We retrospectively analyzed 3,062 PICU patients (247 IFIs, 8.1%) and used clinical features, comorbidities, and laboratory indicators to build binary classification models with multiple machine learning algorithms. In the Test Group List, Diagnosis of fungi1 represents the diagnostic grouping used for data analysis, and missing data indicate that the relevant clinical tests were not performed.External Verification Form refers to the dataset used for external validation. Invasive fungal infections; Pediatric Intensive Care Unit; albumin Ting Wang PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset Science Data Bank