SynthesisInternational journal of environmental research and public health2021
Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.
Synthesis in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06795880 ("Artificial Intelligence-Based Data Analysis Results and Mortality Prediction in Covid-19 Patients in Intensive Care"), which is not on this map. Cited by 15 papers.
What it found
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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.
"Artificial Intelligence-Based Data Analysis Results and Mortality Prediction in Covid-19 Patients in Intensive Care"
Who cites it
15 citing papers in PubMed.
- A Novel Mobile Health Software Platform to Improve Emergency Medical Systems: 912Rwanda.Research square · 2026Article
- An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics.Archives of public health = Archives belges de sante publique · 2025Article
- Exploring Applications of Artificial Intelligence in Critical Care Nursing: A Systematic Review.Nursing reports (Pavia, Italy) · 2025Review
- Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.IEEE open journal of engineering in medicine and biology · 2024Article
- Artificial intelligence and machine learning in prehospital emergency care: A scoping review.iScience · 2023Article
- Artificial Intelligence in the Intensive Care Unit: Present and Future in the COVID-19 Era.Journal of personalized medicine · 2023Review
- Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.Cureus · 2023Review
- Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study.Journal of cardiovascular development and disease · 2023Article
- Digitalization of prevention and treatment and the combination of western and Chinese medicine in management of acute heart failure.Frontiers in cardiovascular medicine · 2023Review
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews.International journal of medical informatics · 2022Review
- Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead.Journal of King Saud University. Computer and information sciences · 2022Review
- Artificial Intelligence for COVID-19 Detection in Medical Imaging-Diagnostic Measures and Wasting-A Systematic Umbrella Review.Journal of clinical medicine · 2022Review
- Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning.Medical & biological engineering & computing · 2022Article
- Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation.JMIR research protocols · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
PubMed holds no abstract for this paper.
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What OpenQuestion holds
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.