ReviewSLAS technology2021
Application of Artificial Intelligence to Address Issues Related to the COVID-19 Virus.
Review in SLAS technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
5 citing papers in PubMed.
- Predicting Risk of Mortality in COVID-19 Hospitalized Patients using Hybrid Machine Learning Algorithms.Journal of biomedical physics & engineering · 2022Article
- Artificial Intelligence and COVID-19: A Systematic umbrella review and roads ahead.Journal of King Saud University. Computer and information sciences · 2022Review
- A framework for information technology-based management against COVID-19 in Iran.BMC public health · 2022Article
- The Role of Artificial Intelligence and Machine Learning Amid the COVID-19 Pandemic: What Lessons Are We Learning on 4IR and the Sustainable Development Goals.International journal of environmental research and public health · 2022Article
- Overview of current state of research on the application of artificial intelligence techniques for COVID-19.PeerJ. Computer science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) plays a major role in addressing novel coronavirus 2019 (COVID-19)-related issues and is also used in computer-aided synthesis planning (CASP). AI, including machine learning, is used by artificial neural networks such as deep neural networks and recurrent networks. AI has been used in activity predictions like physicochemical properties. Machine learning in de novo design explores the generation of fruitful, biologically active molecules toward expected or finished products. Several examples establish the strength of machine learning or AI in this field. AI techniques can significantly improve treatment consistency and decision making by developing useful algorithms. AI is helpful not only in the treatment of COVID-19-infected patients but also for their proper health monitoring. It can track the crisis of COVID-19 at different scales, such as medical, molecular, and epidemiological applications. It is also helpful to facilitate the research on this virus by analyzing the available data. AI can help in developing proper treatment regimens, prevention strategies, and drug and vaccine development. Combination with synthesis planning and ease of synthesis are feasible, and more and more automated drug discovery by computers is expected in the near future to eradicate the COVID-19 virus.
Indexed as
Identifiers
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.