ArticleScientific reports2021
Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 11 of them syntheses that pooled it.
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The trial behind it
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Who cites it
70 citing papers in PubMed, 11 syntheses or guidelines pooled it, 115 citations in OpenAlex.
- Predictive Modeling of Acute Respiratory Distress Syndrome Using Machine Learning: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Accuracy of artificial intelligence algorithms in predicting acute respiratory distress syndrome: a systematic review and meta-analysis.BMC medical informatics and decision making · 2025Pooled it
- A bibliometric analysis of artificial intelligence research in critical illness: a quantitative approach and visualization study.Frontiers in medicine · 2025Pooled it
- Bone Mineral Density, Bone Biomarkers, and Joints in Acute, Post, and Long COVID-19: A Systematic Review.Viruses · 2024Pooled it
- A systematic review of machine learning models for management, prediction and classification of ARDS.Respiratory research · 2024Pooled it
- Global prevalence of COVID-19-induced acute respiratory distress syndrome: systematic review and meta-analysis.Systematic reviews · 2023Pooled it
- The effects of hypertension on the prognosis of coronavirus disease 2019: a systematic review and meta-analysis on the interactions with age and antihypertensive treatment.Journal of hypertension · 2022Pooled it
- Higher Risk of Acute Respiratory Distress Syndrome and Risk Factors among Patients with COVID-19: A Systematic Review, Meta-Analysis and Meta-Regression.International journal of environmental research and public health · 2022Pooled it
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Serum CK-MB, COVID-19 severity and mortality: An updated systematic review and meta-analysis with meta-regression.Advances in medical sciences · 2021Pooled it
- Prevalence and outcomes of co-infection and superinfection with SARS-CoV-2 and other pathogens: A systematic review and meta-analysis.PloS one · 2021Pooled it
- Risk Factors for Barotrauma with Extra-Alveolar Air in a Selected COVID-19 Patient Population: Experience from a Tertiary University Hospital.Journal of clinical medicine · 2026Article
- Article
- Detection of drug-induced acute respiratory distress syndrome risk signals in FAERS: a real-world pharmacovigilance study.Frontiers in medicine · 2026Article
- Infectious Disease Surveillance in the Era of Big Data and AI: Opportunities and Pitfalls.Cureus · 2025Review
- A nomogram for individualized prediction of acute respiratory distress syndrome in patients with severe traumatic brain injury: a retrospective cohort study.BMC pulmonary medicine · 2025Article
- Machine Learning and Artificial Intelligence for Infectious Disease Surveillance, Diagnosis, and Prognosis.Viruses · 2025Review
- IoT-based bed and ventilator management system during the COVID-19 pandemic.Scientific reports · 2025Article
- Clinical Impact of Neutrophil Variation on COVID-19 Complications.Diagnostics (Basel, Switzerland) · 2025Article
- SARS-CoV-2 Impairs Osteoblast Differentiation Through Spike Glycoprotein and Cytokine Dysregulation.Viruses · 2025Article
10 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.
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Registered trials
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.