发表机构
Platform of Bioinformatics; Lausanne University Hospital; Faculty of Biology and Medicine; University of Lausanne; Leenaards Memory Centre; Department of Clinical Neurosciences（生物信息平台; 洛桑大学医院; 生物学与医学学院; 洛桑大学; Leenaards记忆中心; 临床神经科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对阿尔茨海默病临床数据问题，提出无插补变压器NITROGEN，通过特定注意力联合建模。经训练和评估，在跨队列任务中优势明显，还引入调整方法。证明无插补学习在队列转移下的区分能力，强调多方面评估模型对临床部署的重要性。
AI 中文摘要
现实世界临床数据的不完整性和异质性阻碍了阿尔茨海默病的准确诊断分类和疾病严重程度预测。传统插补策略存在诸多问题。本文提出无插补变压器NITROGEN，通过掩码和样本间注意力联合建模患者内特征依赖和患者间关系结构。在ADNI上训练并在OASIS - 3和AIBL上评估，结果显示其在跨队列和任务中有优势，还介绍了模态感知不确定性调整。结果表明无插补注意力学习在队列转移下保持了有意义的区分能力，且评估模型时多方面考量很重要。
英文摘要
Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left unaddressed, these barriers prevent reliable disease modelling and hinder effective clinical evaluation. Conventional imputation strategies introduce systematic bias, distort inter-feature relationships, and yield overconfident predictions, limitations especially consequential in diagnostic settings. Here, we propose NITROGEN, an imputation-free transformer that jointly models within-patient feature dependencies and between-patient relational structure through masked and intersample attention, enabling robust multimodal learning directly from partially observed records. We trained NITROGEN on ADNI (N=7858 scans), and evaluated it on two independent cohorts: OASIS-3 (N=2675 scans) and AIBL (N=1286 scans). Across cohorts and diagnostic and cognitive score prediction tasks, NITROGEN showed robust calibration and uncertainty quantification advantages over tree-based ensemble methods, while maintaining competitive discriminative performance. Cross-cohort and cross-method analyses identified cortical thickness in the temporal pole, age, and APOE genotype as important, though not individually sufficient, features for AD classification. We further introduced a modality-aware uncertainty adjustment that augments predictive uncertainty proportionally to the importance of absent modalities, enabling calibrated confidence when diagnostic information is unavailable. Together, our results show that imputation-free attention learning preserved meaningful discrimination under cohort shift, revealing expected degradation on more distributionally different cohorts, and demonstrate that evaluating models along calibration, interpretability, and cross-cohort reliability, not accuracy alone, is essential for clinical deployment.
