ArticleBMC infectious diseases2026
Deep learning on meta-analytic data for therapeutic decision-making in central nervous system aspergillosis.
Article in BMC infectious diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCentral 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.
methodsWe 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.
resultsIn a retrospective cohort of 64 patients, the contextual LinUCB policy outperformed ε-greedy and a random recommender, achieving the highest average predicted survival rate of 76.5% (max 83.0%, min 67.0%) versus 56.7% for ε-greedy and 49.3% for Random; the observed survival under clinician decisions was 50%. Feature ablation demonstrated that removing sex, clinical presentation, and imaging findings reduced the estimated survival by 19.6% points from an 83.3% baseline, whereas other features (e.g., age, country) each had smaller effects (< 10% points). The LinUCB adaptive policy demonstrated superior cumulative survival gain compared to random and ε-greedy strategies, indicating that incorporating patient context materially improves treatment recommendations.
conclusionIntegrating 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. CLINICAL
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