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[Submitted on 17 Jun 2026] Title:Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes View a PDF of the paper titled Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes, by Izzy Chaiken and 4 other authors View PDF HTML (experimental) Abstract:Invasive mechanical ventilation is a lifesaving therapy, but timely, safe discontinuation is essential to preventing extubation failure (EF) and related risks to health. We present a novel approach to EF prediction that leverages features classified in free-text respiratory therapy notes using a large language model and logistic regression pipeline. Applied to a patient cohort from University of Washington Medicine, our method identifies clinically meaningful EF-related features that improve EF prediction performance when included alongside structured patient data. We further highlight how differences in target populations in prior EF prediction studies, such as heterogenous inclusion criteria and EF definition, can lead to systematic differences in model performance and hinder generalizability between studies. Comments: Published in CHIL 2026. 11 pages, 4 figures, 4 tables, 25 pages including citations and supplemental material Subjects: Computation and Language (cs.CL); Machine Learning (cs.LG); Physics and Society (physics.soc-ph) ACM classes: I.2.7; J.3 Cite as: arXiv:2609.17532 [cs.CL] (or arXiv:2609.17532v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.17532 arXiv-issued DOI via DataCite Submission history From: Izzy Chaiken [view email] [v1] Wed, 17 Jun 2026 16:50:09 UTC (697 KB) Full-text links: Access Paper: View a PDF of the paper titled Enhancing Extubation Failure Prediction with LLM-Derived Features from Respiratory Therapy Clinical Notes, by Izzy Chaiken and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.LG physics physics.soc-ph References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: 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?) 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?)