Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication
Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient. This assumption fails when treatment decisions make the clinically relevant outcome permanently unobservable. As a case study of this problem, we consider post-cardiac-arrest neurological prognostication using a cohort of 2,497 patients, including 1,429 patients whose outcomes were rendered indeterminate by treatment decisions. These patients with indeterminate outcomes were revi
Record details
Published: 12 August 2026
Source: arXiv cs.LG
Category: Research
Topics: Healthcare
Retrieved: 14 August 2026
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ethics.ai (12 August 2026), “Learning Under Treatment-Induced Label Indeterminacy with Expert Annotations of Counterfactual Outcomes: A Case Study in Neurological Prognostication,” evidence record 19477, https://ethics.ai/record/19477 (originally published by arXiv cs.LG).
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