The paper proposes a novel approach of survival transformers and extreme gradient boosting models in predicting cognitive deterioration in individuals with mild cognitive impairment (MCI) using metabolomics data in the ADNI cohort. By leveraging advanced machine learning and transformer-based techniques applied in survival analysis, the proposed approach highlights the potential of these techniques for more accurate early detection and intervention in Alzheimer's dementia disease. This research also underscores the importance of non-invasive biomarkers and innovative modelling tools in enhancing the accuracy of dementia risk assessments, offering new avenues for clinical practice and patient care. A comprehensive Monte Carlo simulation procedure consisting of 100 repetitions of a nested cross-validation in which models were trained and evaluated, indicates that the survival machine learning models based on Transformer and XGBoost achieved the highest mean C-index performances, namely 0.85 and 0.8, respectively, and that they are superior to the conventional survival analysis Cox Proportional Hazards model which achieved a mean C-Index of 0.77. Moreover, based on the standard deviations of the C-Index performances obtained in the Monte Carlo simulation, we established that both survival machine learning models above are more stable than the conventional statistical model.

翻译：本文提出了一种新颖的方法，结合生存Transformer与极端梯度提升模型，利用ADNI队列中的代谢组学数据预测轻度认知障碍（MCI）个体的认知功能恶化。通过将先进的机器学习与基于Transformer的技术应用于生存分析，该方法凸显了这些技术在阿尔茨海默病早期更精准检测与干预方面的潜力。本研究同时强调了非侵入性生物标志物与创新建模工具在提升痴呆风险评估准确性方面的重要性，为临床实践与患者护理提供了新途径。一项包含100次重复的嵌套交叉验证的综合蒙特卡洛模拟流程表明，基于Transformer和XGBoost的生存机器学习模型取得了最高的平均C指数性能，分别为0.85和0.80，且优于传统生存分析Cox比例风险模型（平均C指数为0.77）。此外，基于蒙特卡洛模拟中获得的C指数性能标准差，我们证实上述两种生存机器学习模型均比传统统计模型具有更高的稳定性。