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arXiv Machine Learning By Priyanka Siddappa, Muntaqim Ahmed Raju, Shifat Haider Al Amin, Ruizhe Ma

Alert-List Concordance for Evaluating Imputation Strategies in ICU Sepsis Prediction

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The paper introduces alert‑list concordance, a metric that evaluates whether different imputation strategies for ICU sepsis prediction models identify the same high‑risk patients, measured by Jaccard similarity of top‑k% alert sets with bootstrap confidence intervals. Using PhysioNet/CinC 2019 data, the authors compare nine imputation methods and a native‑missingness baseline, finding that many methods have similar AUROC yet produce alert lists that overlap only 51–75% of the time. ForwardFill stands out as an outlier, achieving higher discrimination but lower overlap (40–45%).

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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