Development of a machine learning-based predictive model for long-term adverse outcomes in neonatal bacterial meningitis

OBJECTIVE: To explore the application of machine learning methods for screening risk factors for long-term adverse prognosis in neonatal bacterial meningitis, determine the final prediction model, and evaluate its predictive value. METHODS: This study included 139 full-term neonates diagnosed with neonatal bacterial meningitis in the capital institute of pediatrics between January 2019 and December 2023. Based on follow-up outcomes, they were divided into a poor prognosis group (n = 45) and a good prognosis group (n = 94). Thirty-three clinical variables were collected. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator, Boruta, and Recursive Feature Elimination. Seven machine learning models were constructed. Model performance was evaluated using metrics including the area under the receiver operating characteristic curve, accuracy, and sensitivity. The Shapley Additive explanation method was used to interpret the models. RESULTS: Among the seven models, The Random Forest model demonstrates the best overall predictive performance, although Logistic Regression achieved the highest discriminative ability (AUC: 0.903), Random Forest was more suitable for clinical application due to its superior accuracy (0.881), better calibration (Brier score: 0.123), and balanced sensitivity (0.887) and specificity (0.878). Shapley Additive explanation interpretability analysis further revealed that the top three important features were cerebrospinal fluid white blood cell count, cerebrospinal fluid protein levels, and seizures. CONCLUSION: Machine learning models, particularly the superior-performing Random Forest, are proven to reliably predict long-term adverse outcomes in NBM patients, aiding in the identification of high-risk individuals. Further validation in broader cohorts is warranted to enhance generalizability and clinical applicability.