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 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 be
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- PossiblePossibly related (embedding) · 46%Open-Source AI Tools for Alzheimer’s - Neuroscience News →
- LinkedLinked via arxiv author · 85%Christelle Schneuwly Diaz →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”
- LinkedLinked via arxiv author · 85%Narmina Baghirova →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”
- LinkedLinked via arxiv author · 85%Duy-Thanh Vu →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”
- LinkedLinked via arxiv author · 85%Duy-Cat Can →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”
- LinkedLinked via arxiv author · 85%Gilles Allali →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”
- LinkedLinked via arxiv author · 85%Philippe Ryvlin →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”
- LinkedLinked via arxiv author · 85%Oliver Y. Chén →
“Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantifica”