A support vector machine-based tool for rapid pediatric asthma exacerbation risk assessment: development and nursing application

Background Childhood asthma poses a significant threat to pediatric health, and traditional assessment methods are often inadequate in efficiency and accuracy. This study aims to develop a rapid assessment tool for pediatric asthma exacerbation risk based on the support vector machine (SVM) algorithm and evaluate its value in nursing practice. Methods Clinical data from children with asthma were collected, incorporating key indicators including eczema, allergic rhinitis (AR), family medical history (FMH), dyspnea, white blood cell count (WBC), immunoglobulin E (IgE), and fractional exhaled nitric oxide (FeNO). An SVM-based risk prediction model was developed. Utilizing Plumber, an application programming interface (API) was constructed to enable data transmission and real-time risk assessment. The pediatric asthma risk rapid tool (PARRT) mini-program was subsequently developed. Service quality metrics were compared before and after PARRT implementation. Results The constructed SVM model demonstrated excellent performance on the test dataset, achieving an area under the curve (AUC) of 0.9998. Clinical application revealed that PARRT significantly reduced patient wait time, decreased report wait time, improved satisfaction scores among patients and their families, as well as enhanced nursing staff efficiency. Conclusion PARRT exhibits strong predictive accuracy and holds considerable promise for clinical utility in pediatric asthma management.