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September 5, 2024Journal of Intensive Care Medicine

Predicting Mortality in Sepsis-Associated Acute Respiratory Distress Syndrome: A Machine Learning Approach Using the MIMIC-III Database

Shengtian Mu Dongli Yan Jie Tang Zhen Zheng

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Abstract

To develop and validate a mortality prediction model for patients with sepsis-associated Acute Respiratory Distress Syndrome (ARDS).

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Cite This Study

Mu et al. (2024) studied this question.

synapsesocial.com/papers/68e5943fb6db64358752f078 https://doi.org/10.1177/08850666241281060

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Keep reading

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

1A novel clinical prediction model for in-hospital mortality in sepsis patients complicated by ARDS: A MIMIC IV database and external validation study2024 · 9 citations
2Machine learning for the early prediction of acute respiratory distress syndrome (ARDS) in patients with sepsis in the ICU based on clinical data2024 · 22 citations
3Machine learning for the early prediction of 28-day mortality in patients with sepsis in the ICU based on MIMIC-Ⅳ databaseOriginal Research · 2026
4Prediction of Sepsis Mortality in ICU Patients Using Machine Learning Methods2024 · 15 citations
5Machine learning-based risk prediction of 28-day mortality for sepsis patients with augmented renal clearanceReview · 2026

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Development and validation of a prognostic model for acute respiratory distress syndrome in critically Ill patients with intra-abdominal sepsis: a multicenter cohort study2026
Prediction of sepsis mortality in ICU patients using machine learning methods2024
Under 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

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