Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval
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Computer Science > Machine Learning
arXiv:2607.19020 (cs)
[Submitted on 21 Jul 2026]
Title:Biological Amnesia in ICU Time-Series Prediction: A Drift-Adaptive Two-Stream Architecture with Temporal Retrieval
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Abstract:Background: Clinical decision support systems degrade silently as treatment protocols evolve, yet standard adaptation methods treat models as monolithic blocks, unable to distinguish stable patient physiology from shifting institutional practice. Methods: We propose an adaptive clinical intelligence architecture for ICU intervention prediction that structurally decouples physiological from treatment representations, confining parameter updates to the treatment stream upon a dual distributional and accuracy trigger. Automated audit logs record which treatment features drove each adaptation event and how their importance shifted. At inference, an attribution-driven Temporal RAG module grounds each prediction in patient-specific, era-matched PubMed evidence anchored to the patient's dominant physiological features. Experiments used 84,792 MIMIC-IV stays (2008-2022) under strict chronological split. Results: Drift localised entirely to the treatment stream, validating the structural prior. Selective adaptation improved vasopressor and septic shock discrimination and calibration over the static source model. A fully retrained baseline yielded marginally higher aggregate discrimination but missed 26 septic shock cases the framework correctly identified, with none in the reverse direction; retrieval consistency with the pre-adaptation source model was preserved by the framework but degraded substantially in the retrained baseline. Conclusions: Structurally constraining adaptation to drifting components while preserving stable physiological representations enables clinical AI to evolve with practice without distorting learned patient biology. This architecture offers a template for governable, interpretable deployment of adaptive models in high-stakes clinical environments.
| Comments: | 10 pages, 3 figures, 8 tables. Code and aggregate audit logs available at: [this https URL]. Under Review |
| 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:2607.19020v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.19020
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: K. M. Merajul Arefin [view email][v1] Tue, 21 Jul 2026 12:07:36 UTC (1,721 KB)
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