ArticleAnnals of translational medicine2021
Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05611177 (Predicting Mortality in Patients With the Acute Respiratory Distress Syndrome Using Machine Learning), which is not on this map. Cited by 28 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Predicting Mortality in Patients With the Acute Respiratory Distress Syndrome Using Machine Learning
Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Early Prediction of Mortality Risk in Acute Respiratory Distress Syndrome: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Development and validation of a clinical risk model to predict the hospital mortality in ventilated patients with acute respiratory distress syndrome: a population-based study.BMC pulmonary medicine · 2022Trial
- Early Detection of Clinical Deterioration in ICU Patients With Respiratory Failure Receiving Vasoactive Support: A Machine Learning Approach to Identifying High-Risk Individuals.Health science reports · 2026Article
- Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.Critical care (London, England) · 2026Review
- Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.BMJ health & care informatics · 2026Article
- A gradient boosting machine model for predicting prognosis in patients with acute respiratory distress syndrome.American journal of translational research · 2026Article
- Diagnostic Codes in AI Prediction Models and Label Leakage of Same-Admission Clinical Outcomes.JAMA network open · 2025Article
- Design and Development of a Machine Learning Model for Predicting ICU Patients' Length of Stay.Cureus · 2025Article
- Early Prediction of ICU Mortality in Patients with Acute Hypoxemic Respiratory Failure Using Machine Learning: The MEMORIAL Study.Journal of clinical medicine · 2025Article
- Every cloud has a silver lining: DeepSeek's light through acute respiratory distress syndrome shadows.Journal of thoracic disease · 2025Article
- Comparison of artificial intelligence and logistic regression models for mortality prediction in acute respiratory distress syndrome: a systematic review and meta-analysis.Intensive care medicine experimental · 2025Review
- Construction of a prediction model for sarcopenic obesity based on machine learning.Frontiers in public health · 2025Article
- An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome.Frontiers in medicine · 2025Article
- Association Between Temperature During Intensive Care Unit and Mortality in Patients With Acute Respiratory Distress Syndrome.Therapeutic hypothermia and temperature management · 2024Article
- ARDSFlag: an NLP/machine learning algorithm to visualize and detect high-probability ARDS admissions independent of provider recognition and billing codes.BMC medical informatics and decision making · 2024Article
- Article
- Machine learning for the prediction of in-hospital mortality in patients with spontaneous intracerebral hemorrhage in intensive care unit.Scientific reports · 2024Article
- Early prediction of mortality at sepsis diagnosis time in critically ill patients by using interpretable machine learning.Journal of clinical monitoring and computing · 2024Article
- Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage.Journal of translational medicine · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTraditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases.
methodsPatients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO
resultsWith the MIMIC-III dataset (308 patients, for comparisons with the existing scoring systems), the RF model predicted the in-hospital mortality, 30-day mortality, and 1-year mortality with an AUROC of 0.891, 0.883, and 0.892, respectively, which were significantly higher than those of the SAPS-II, APPS, OSI, and OI (all P<0.001). In the multi-source validation (the combined dataset of 2,235 patients in MIMIC-III and 331 patients in eICU-CRD), the RF model achieved an AUROC of 0.905 and 0.736 for predicting in-hospital mortality for the MIMIC-III and eICU-CRD datasets, respectively. The calibration plots suggested good fits for our RF model and these scoring systems for predicting mortality. The platelet count and lactate level were the strongest predictive variables for predicting in-hospital mortality.
conclusionsCompared to the existing scoring systems, machine learning significantly improved performance for predicting ARDS mortality. Validation with multi-source datasets showed a relatively robust generalisation ability of our prediction model.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.