BACKGROUND: The prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of children. In this study, we aim to systematically evaluate the value of routine blood parameters in predi...
The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesio...
The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning t...
Severe Mycoplasma pneumoniae pneumonia (SMPP) poses significant diagnostic challenges due to its clinical features overlapping with those of other common respiratory diseases. This study aims to develop and validate machine learning (ML) models for t...
OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide reliable factors for predicting the clinical type of the disease.
BACKGROUND: Pneumonia is a major threat to the health of children, especially those under the age of five. Mycoplasma pneumoniae infection is a core cause of pediatric pneumonia, and the incidence of severe mycoplasma pneumoniae pneumonia (SMPP) has...
To study the differences in the urine metabolome between pediatric patients with severe Mycoplasma pneumoniae pneumonia (SMPP) and those with general Mycoplasma pneumoniae pneumonia (GMPP) via non-targeted metabolomics method, and potential biomarker...