Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts.

Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the pervasive 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 that are 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 the Alzheimer's Disease Neuroimaging Initiative (ADNI; N=7858 scans), and evaluated it, without further retraining, on two independent cohorts: the Open Access Series of Imaging Studies (OASIS-3; N=2675 scans) and the Australian Imaging, Biomarkers and Lifestyle (AIBL; N=1286 scans) study. Across all cohorts and various tasks including binary and multi-class diagnostic classification as well as continuous cognitive score prediction, NITROGEN showed robust probability calibration and uncertainty quantification advantages over tree-based ensemble methods, while maintaining competitive discriminative and continuous cognitive score prediction performance. Moreover, 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 robust AD status classification. To address prediction reliability under incomplete data, we further introduced a modality-aware uncertainty adjustment that augments predictive uncertainty in proportion to the estimated importance of absent modalities, enabling models to express calibrated confidence when critical diagnostic information is unavailable. Taken together, our results show that imputation-free attention learning preserved meaningful discrimination under external cohort shift, while revealing the expected performance degradation on more distributionally different cohorts. These findings further demonstrate that evaluating machine learning models for neurological disease along dimensions of calibration, interpretability, and cross-cohort reliability-not predictive accuracy alone-is essential for better clinical deployment. Code to reproduce our experiments is publicly available at https://github.com/cschneuw/nitrogen.