Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.

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Bibliographic Details
Title: Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.
Authors: Piasecki A; Department of Anesthesiology and Intensive Care, Institute of Clinical Sciences at the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. adam.piasecki@gu.se., Adamczyk K; Department of Anesthesiology and Intensive Care, National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland., Śniegowski M; Department of Computer Science, National Medical Institute of the Ministry of the Interior and Administration, Warsaw, Poland., Mandarano R; Department of Anesthesiology and Intensive Care, Careggi University Hospital, Florence, Italy., Brunoni B; Department of Medicine and Surgery, University of Milan-Bicocca, Monza, Monza-Brianza, Italy., de Grooth HJ; Intensive Care Center, UMC Utrecht, Utrecht, The Netherlands., Elbers PWG; Department of Intensive Care Medicine, Amsterdam Medical Data Science, Amsterdam Public Health, Amsterdam UMC, Vrije Universiteit, Amsterdam, The Netherlands.
Source: Scientific reports [Sci Rep] 2026 Jun 25; Vol. 16 (1). Date of Electronic Publication: 2026 Jun 25.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-026-59064-y