When uncertainty quantification fails: structural vulnerabilities and corrections for selective prediction under clinical class imbalance
[Uczenie maszynowe] Autorzy: Walter Endrizzi, Flavio Ragni, Raffaele Marchesi i 4 więcej
Background. Selective prediction systems improve the safety of clinical artificial intelligence by deferring ambiguous cases to human experts while automating high-confidence decisions. However, this safety mechanism relies entirely on the premise that uncertainty scores accurately reflect clinical ambiguity. In prognostic tasks characterized by severe class imbalance, where the clinical outcome of interest is a minority, this assumption breaks down. Standard uncertainty measures cease to capture clinically aligned ambiguity; instead, they become a proxy for class membership, systematically penalizing minority patients regardless of their individual predictive clarity. Results. Across 56 synthetic conditions and three neurodegenerative cohorts spanning severe class imbalance to near-balanced conditions (multiple sclerosis, Parkinson's Disease, Alzheimer's Disease), our framework consist…
- Kategoria arxiv
- cs.LG
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- Uczenie maszynowe