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March 1, 2024HeliyonOpen Access

Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data

Zhenzhen Jiang Leping Liu Lin Du Shanshan Lv Liang Fang Yanwei Luo Chunjiang Wang Qin Shen

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Abstract

Acute respiratory distress syndrome (ARDS) is a fatal outcome of severe sepsis. Machine learning models are helpful for accurately predicting ARDS in patients with sepsis at an early stage.

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Cite This Study

Jiang et al. (2024) studied this question.

synapsesocial.com/papers/68e761c9b6db6435876d7c93 https://doi.org/10.1016/j.heliyon.2024.e28143

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Early prediction of ARDS caused by non-pulmonary sepsis based on machine learning algorithms of inflammatory indicators and blood gas parameters2025 · 2 citations
2Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database2024 · 10 citations
3Under the background of the new global definition of ARDS: an interpretable machine learning approach for predicting 28-day ICU mortality in patients with sepsis complicated by ARDS2025 · 4 citations
4Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study2024 · 26 citations
5Development and validation of a prognostic model for acute respiratory distress syndrome in critically Ill patients with intra-abdominal sepsis: a multicenter cohort studyCohort Study · 2026 · 1 citations

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