Optimizing Antibiotics Prophylaxis in Neurosurgery through Machin Learning: Predicting Infections and Personalizing Treatment Strategies.

Gamification and Augmented Reality., Journal Year: 2025, Volume and Issue: 3, P. 108 - 108

Published: April 4, 2025

Introduction: Preventing postoperative infections in neurosurgery is crucial to reducing morbidity. Machine learning (ML) models have shown potential predicting and optimizing antibiotic use. Methods: Patient data from neurosurgical procedures were analyzed develop evaluate ML for infections. Various algorithms, including logistic regression, Random Forest, Gradient Boosting (GBM), SVM, neural networks, compared. Performance metrics such as accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC-ROC) calculated. Results: The GBM model achieved best performance, with an accuracy of 89.1% AUC-ROC 0.91. most important predictors infection surgical duration (27.3%), preoperative CRP levels (21.8%), blood loss (18.5%). Patients who developed had significantly longer surgeries elevated levels. Conclusions: demonstrated high neurosurgery. Early identification high-risk patients may optimize prophylaxis reduce complications. Further validation required clinical implementation.

Language: Английский

---

AI-Enhanced Threat Intelligence for Proactive Zero-Day Attack Detection

Gamification and Augmented Reality., Journal Year: 2025, Volume and Issue: 3, P. 112 - 112

Published: April 13, 2025

Introduction: zero-day attacks pose a critical cybersecurity challenge by targeting vulnerabilities that are undisclosed to software vendors and security experts. Conventional threat intelligence approaches, which rely on known signatures attack patterns, often fail detect these stealthy threats.Methods: this study proposes comprehensive framework combines AI technologies, including machine learning algorithms, natural language processing (NLP), anomaly detection, analyze threats in real time. The incorporates predictive modeling anticipate potential vectors automated response mechanisms enable rapid mitigation.Results: the findings indicate AI-enhanced significantly improves detection of compared traditional methods. reduces time enhances accuracy identifying subtle anomalies indicative exploits.Conclusion: research highlights transformative strengthening against attacks. By leveraging advanced real-time analytics, proposed offers more robust adaptive approach cybersecurity.

Language: Английский

---

AI-Deep Learning Framework for Predicting Neuropsychiatric Outcomes Following Toxic Effects of Drugs on The Brain

Multidisciplinar, Journal Year: 2025, Volume and Issue: 3, P. 222 - 222

Published: April 25, 2025

Introduction: drug-induced neurotoxicity represents a significant clinical challenge, with neuropsychiatric complications affecting treatment outcomes and patient quality of life. Current predictive tools lack both accuracy interpretability, limiting their utility. Methods: We developed hybrid CNN-LSTM deep learning framework attention mechanisms, trained on multimodal data including electronic health records, neuroimaging, biomarker profiles. Model interpretability was achieved through SHAP value analysis, performance evaluated via 5-fold cross-validation.Results: The model 92 % (AUC-ROC 0,93), significantly outperforming traditional approaches. Key predictors included drug dosage (SHAP=0,15), duration (SHAP=0,12), age. High-risk subgroups (patients >60 years) showed 2,5× increased risk cognitive decline (p<0,01).Conclusions: This interpretable AI enables precise, clinically actionable prediction following neurotoxicity, supporting personalized decisions mitigation strategies.

Language: Английский

---