ReviewNeural computing & applications2022
Machine learning applications for COVID-19 outbreak management.
Review in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 131 citations in OpenAlex.
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Ethical and Legal Concerns of Deepfake Technology in Biomedical Imaging: A Comprehensive Survey.Annals of biomedical engineering · 2026Review
- Enhancing COVID-19 Forecasting Accuracy in Malaysia Using a Hybrid ARIMA-LSTM Model With Exogenous Variables: A Time-Series Predictive Study.Health science reports · 2026Article
- The 3C dataset: A comprehensive dataset for COVID-19 cardiac complications diagnosis.Data in brief · 2026Article
- Opportunities and Challenges in Gas Sensor Technologies for Accurate Detection of COVID-19.Biosensors · 2025Review
- A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.Scientific reports · 2025Article
- Comparative Evaluation and Performance of Large Language Models in Clinical Infection Control Scenarios: A Benchmark Study.Healthcare (Basel, Switzerland) · 2025Article
- COVID-19 risk stratification among older adults: a machine learning approach to identify personal and health-related risk factors.BMC public health · 2025Article
- IMPACT: an interactive multi-disease prevention and counterfactual treatment system using explainable AI and a multimodal LLM.PeerJ. Computer science · 2025Article
- Hybrid ARIMA-LSTM for COVID-19 forecasting: a comparative AI modeling study.PeerJ. Computer science · 2025Article
- A dynamic approach to support outbreak management using reinforcement learning and semi-connected SEIQR models.BMC public health · 2024Article
- Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.Heliyon · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.AIMS public health · 2024Article
- Flavonoid-metal ion Complexes as Potent Anticancer Metallodrugs: A Comprehensive Review.Mini reviews in medicinal chemistry · 2024Review
- Imaging and Non-imaging Analytical Techniques Used for Drug Nanosizing and their Patents: An Overview.Recent patents on nanotechnology · 2024Review
- Intolerance of uncertainty and mental health in China "Post-pandemic" age: The mediating role of difficulties in emotion regulation.PloS one · 2024Article
- Learner satisfaction with synchronous and asynchronous virtual learning systems during the COVID-19 pandemic in Tehran university of medical sciences: a comparative analysis.BMC medical education · 2023Article
- Enhancing COVID-19 Classification Accuracy with a Hybrid SVM-LR Model.Bioengineering (Basel, Switzerland) · 2023Article
- Artificial Intelligence in Evaluation of Permanent Impairment: New Operational Frontiers.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the COVID-19 epidemic has resulted in millions of deaths and has impacted practically every area of human life. Several machine learning (ML) approaches are employed in the medical field in many applications, including detecting and monitoring patients, notably in COVID-19 management. Different medical imaging systems, such as computed tomography (CT) and X-ray, offer ML an excellent platform for combating the pandemic. Because of this need, a significant quantity of study has been carried out; thus, in this work, we employed a systematic literature review (SLR) to cover all aspects of outcomes from related papers. Imaging methods, survival analysis, forecasting, economic and geographical issues, monitoring methods, medication development, and hybrid apps are the seven key uses of applications employed in the COVID-19 pandemic. Conventional neural networks (CNNs), long short-term memory networks (LSTM), recurrent neural networks (RNNs), generative adversarial networks (GANs), autoencoders, random forest, and other ML techniques are frequently used in such scenarios. Next, cutting-edge applications related to ML techniques for pandemic medical issues are discussed. Various problems and challenges linked with ML applications for this pandemic were reviewed. It is expected that additional research will be conducted in the upcoming to limit the spread and catastrophe management. According to the data, most papers are evaluated mainly on characteristics such as flexibility and accuracy, while other factors such as safety are overlooked. Also, Keras was the most often used library in the research studied, accounting for 24.4 percent of the time. Furthermore, medical imaging systems are employed for diagnostic reasons in 20.4 percent of applications.
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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.