Drift-Adaptive ICU Intervention Prediction: Freezing the Physiological Encoder for Auditable Model Updating

[2607.19020] Audit Before You Update: Recording Artifacts and the Limits of Bounded Model Updates in MIMIC-IV ICU Prediction Skip to main content Search Submit Donate Log in Computer Science > Machine Learning arXiv:2607.19020 (cs) [Submitted on 21 Jul 2026 ( v1 ), last revised 11 Oct 2026 (this version, v5)] Title: Audit Before You Update: Recording Artifacts and the Limits of Bounded Model Updates in MIMIC-IV ICU Prediction Authors: Fatema Ferdous Tamanna , K. M. Merajul Arefin , Md. Abdul Masud View PDF HTML (experimental) Abstract: Clinical prediction models in intensive care degrade as data shift, and regulators such as the FDA ask developers to specify bounded update protocols in advance. Evaluations of such protocols assume that apparent drift reflects clinical practice rather than changes in how the database records care. We test whether update strategies can be evaluated on unaudited ICU data, and whether a parameter-bounded update gives up accuracy or attribution stability relative to full fine-tuning once the data are audited. We audit MIMIC-IV 3.1 (2008-2022) for recording artifacts and define a cleaning protocol. On the cleaned cohort, a label-free classifier two-sample test gates updates across three eras. We compare a bounded update, which retrains only treatment-context and fusion parameters, with full fine-tuning for two model families and two targets under a pre-specified non-inferiority margin of 0.015 AUROC, and measure attribution stability against the source model. In 2020-2022 about half of ICU stays have no input or procedure-event records, and a vasopressor ItemID migration makes the 2017-2019 label rate appear to fall when it actually rises. On the cleaned cohort, the gate detects drift in every era, including in physiology-only features, with no false alarms on null data. Bounded updating is non-inferior or superior in 10 of 12 primary cells, but this depends on the target, the model family and learning-rate tuning, and adaptation gains over never updating are small. At a shared learning rate, bounded updates keep physiological attributions more stable in neural models, clearly for vasopressor and less clearly for intubation; with separately tuned rates, full updates are more stable. Audits should precede update evaluation, and the update boundary should be chosen per target. Comments: Major revision: Added dataset temporal-validity audit, multi-era drift gating, non-inferiority testing for bounded updates, and procedural evaluations of XAI attribution stability across varied tuning setups Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Quantitative Methods (q-bio.QM) ACM classes: I.2.6; J.3 Cite as: arXiv:2607.19020 [cs.LG] (or arXiv:2607.19020v5 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2607.19020 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Fatema Tamanna [ view email ] [v1] Tue, 21 Jul 2026 12:07:36 UTC (1,721 KB) [v2] Thu, 20 Aug 2026 14:42:35 UTC (1,825 KB) [v3] Wed, 26 Aug 2026 21:18:12 UTC (1,827 KB) [v4] Sun, 13 Sep 2026 11:55:05 UTC (1,822 KB) [v5] Sun, 11 Oct 2026 19:37:46 UTC (2,221 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Additional Features Audio Summary Current browse context: cs.LG < prev | next > new | recent | 2026-07 Change to browse by: cs cs.AI cs.IR q-bio q-bio.QM References & Citations SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab)

Related documents

• Drift-Adaptive ICU Intervention Prediction: Freezing the Physiological Encoder for Auditable Model Updating#674489
• PRAM: Post-hoc Retrieval Augmentation for Parameter-Free Domain Adaptation of ICU Clinical Prediction Models#156678
• PRAM: Post-hoc Retrieval Augmentation for Parameter-Free Domain Adaptation of ICU Clinical Prediction Models#152667
• STREAM: A data-driven framework for physiological state monitoring in ICU patients.#1166954
• Development and Validation of a Deep Learning Model for Prediction of Adult Physiological Deterioration#1078971