Deep Learning on Meta-Analytic Data for Therapeutic Decision-Making in Central Nervous System Aspergillosis

Background Central nervous system (CNS) aspergillosis is a rare but highly fatal infection, particularly among immunocompromised individuals. Timely diagnosis and optimal treatment selection are crucial for improving patient outcomes, yet clinical decision-making remains challenging. Methods We integrated clinical data from 64 published CNS aspergillosis cases (2014–2024) and structured electronic medical records (EMRs) from 200 ICU patients. After preprocessing (one-hot encoding, Z-score standardization, BERT-based text feature extraction), a Gradient Boosting Classifier (GBC) was trained to predict 30-day survival. Additionally, a LinUCB-based adaptive treatment policy was developed to dynamically optimize therapy choices. Model performance was evaluated against logistic regression, random forest models, and baseline treatment policies. Results The GBC model achieved 83% accuracy in predicting 30-day survival, outperforming logistic regression (72%) and random forests (78%). Key mortality predictors included older age, multiple CNS lesions, and delayed antifungal therapy. Feature ablation analysis confirmed the critical impact of clinical presentation, imaging findings, and treatment delay. The LinUCB adaptive policy demonstrated superior cumulative survival gain compared to random and ε-greedy strategies, achieving a stabilized survival probability of 0.81 by simulation step 300. Conclusion Integrating meta-analytic and EMR-derived data with machine learning models can accurately predict survival and inform adaptive treatment strategies in CNS aspergillosis. The proposed LinUCB-guided approach offers a promising framework for real-time, personalized decision-making in critically ill patients.