ArticleStatistics in biopharmaceutical research2020
Machine learning for clinical trials in the era of COVID-19.
Article in Statistics in biopharmaceutical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 70 citations in OpenAlex.
- Effects of Transcranial Direct Current Stimulation on Cognitive Deficits in Depression: A Systematic Review.Psychiatry and clinical psychopharmacology · 2023Pooled it
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Repurposing of Anti-Infectives for the Management of Onchocerciasis Using Machine Learning and Protein Docking Studies.Bioinformatics and biology insights · 2025Article
- The impact of heterogeneity on the analysis of platform trials with normally distributed outcomes.BMC medical research methodology · 2024Article
- TrialView: An AI-powered Visual Analytics System for Temporal Event Data in Clinical Trials.Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences · 2024Article
- Bridging the Worlds of Pharmacometrics and Machine Learning.Clinical pharmacokinetics · 2023Review
- A Review of the Machine Learning Algorithms for Covid-19 Case Analysis.IEEE transactions on artificial intelligence · 2023Article
- Artificial Intelligence Applied to clinical trials: opportunities and challenges.Health and technology · 2023Review
- The medical and societal impact of big data analytics and artificial intelligence applications in combating pandemics: A review focused on Covid-19.Social science & medicine (1982) · 2022Review
- Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction.Journal of personalized medicine · 2021Article
- Automated machine learning optimizes and accelerates predictive modeling from COVID-19 high throughput datasets.Scientific reports · 2021Article
- How artificial intelligence and machine learning can help healthcare systems respond to COVID-19.Machine learning · 2021Article
- Intelligent system for COVID-19 prognosis: a state-of-the-art survey.Applied intelligence (Dordrecht, Netherlands) · 2021Article
- A Framework for Inferring Epidemiological Model Parameters using Bayesian Nonparametrics.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Computer-Aided-Diagnosis as a Service on Decentralized Medical Cloud for Efficient and Rapid Emergency Response Intelligence.New generation computing · 2021Article
- Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets.PeerJ. Computer science · 2021Article
- Article
- Applications of artificial intelligence in battling against covid-19: A literature review.Chaos, solitons, and fractals · 2021Review
- Statistical Challenges in the Conduct and Management of Ongoing Clinical Trials During the COVID-19 Pandemic.Statistics in biopharmaceutical research · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The world is in the midst of a pandemic. We still know little about the disease COVID-19 or about the virus (SARS-CoV-2) that causes it. We do not have a vaccine or a treatment (aside from managing symptoms). We do not know if recovery from COVID-19 produces immunity, and if so for how long, hence we do not know if "herd immunity" will eventually reduce the risk or if a successful vaccine can be developed - and this knowledge may be a long time coming. In the meantime, the COVID-19 pandemic is presenting enormous challenges to medical research, and to clinical trials in particular. This paper identifies some of those challenges and suggests ways in which machine learning can help in response to those challenges. We identify three areas of challenge: ongoing clinical trials for non-COVID-19 drugs; clinical trials for repurposing drugs to treat COVID-19, and clinical trials for new drugs to treat COVID-19. Within each of these areas, we identify aspects for which we believe machine learning can provide invaluable assistance.
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.