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

arXiv:2607. 11656v1 Announce Type: cross Abstract: Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

• llms
• multimodal
• safety