De-identified clinical dataset for: Identification of Clinical Phenotypes and Prediction Model for High-Risk Phenotypes of Pediatric Community-Acquired Pneumonia Based on Unsupervised Machine Learning

This dataset contains the de-identified clinical data used in the manuscript "Identification of Clinical Phenotypes and Prediction Model for High-Risk Phenotypes of Pediatric Community-Acquired Pneumonia Based on Unsupervised Machine Learning".<br>Background: The study aimed to identify distinct clinical phenotypes in pediatric community-acquired pneumonia (CAP) patients and develop a prediction model for high-risk phenotypes using unsupervised machine learning.<br>Data Source: The data was retrospectively collected from electronic medical records of pediatric CAP patients who underwent bronchoalveolar lavage at two hospitals in Fujian Province, China.<br>Data Content: The dataset includes de-identified patient-level data on:- Demographic characteristics (e.g., age, gender)- Pathogen detection results (from BALF NGS, throat swab PCR, serology)- Inflammatory markers (WBC, NLR, CRP, PCT, LDH, D-dimer)- Imaging features- Clinical outcomes (length of hospital stay)- The assigned clinical phenotypes (MP-Dominant, Mixed-Infection, High-Inflammation) derived from k-prototypes clustering analysis.<br>Usage Notes: This dataset is sufficient to reproduce the clustering analysis and predictive modeling results reported in the associated manuscript. All personally identifiable information has been removed to protect patient privacy.<br><br>