ReviewBriefings in bioinformatics2021
Text mining approaches for dealing with the rapidly expanding literature on COVID-19.
Review in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 3 of them syntheses 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
35 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Global Research on Pandemics or Epidemics and Mental Health: A Natural Language Processing Study.Journal of epidemiology and global health · 2024Pooled it
- Transforming epilepsy research: A systematic review on natural language processing applications.Epilepsia · 2023Pooled it
- Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer.BMC medical informatics and decision making · 2022Pooled it
- Exploring Research Trends in Chronic Kidney Disease and Sedentary Behavior: A Text Mining Analysis of Article Titles.Cureus · 2026Review
- Word embeddings as autonomous predictors in materials design-the effect of inherent variability on information transfer.Journal of cheminformatics · 2026Article
- Darling (v2.0): Mining disease-related databases for the detection of biomedical entity associations.Computational and structural biotechnology journal · 2025Article
- Identification of potential hub genes and drugs in septic kidney injury: a bioinformatic analysis with preliminary experimental validation.Frontiers in medicine · 2025Article
- Artificial Intelligence in Medical Affairs: A New Paradigm with Novel Opportunities.Pharmaceutical medicine · 2024Review
- BugSigDB captures patterns of differential abundance across a broad range of host-associated microbial signatures.Nature biotechnology · 2024Article
- PheSeq, a Bayesian deep learning model to enhance and interpret the gene-disease association studies.Genome medicine · 2024Article
- The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses.Systematic reviews · 2024Article
- Searching for Essential Genes and Targeted Drugs Common to Breast Cancer and Osteoarthritis.Combinatorial chemistry & high throughput screening · 2024Article
- Using Social Media to Help Understand Patient-Reported Health Outcomes of Post-COVID-19 Condition: Natural Language Processing Approach.Journal of medical Internet research · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Enhancing biomedical search interfaces with images.Bioinformatics advances · 2023Article
- In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools.Pharmaceutics · 2022Review
- Clinical Application of Detecting COVID-19 Risks: A Natural Language Processing Approach.Viruses · 2022Article
- e-TSN: an interactive visual exploration platform for target-disease knowledge mapping from literature.Briefings in bioinformatics · 2022Article
- A COVID-19 Search Engine (CO-SE) with Transformer-based architecture.Healthcare analytics (New York, N.Y.) · 2022Article
- COVID-19-Related Scientific Literature Exploration: Short Survey and Comparative Study.Biology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.
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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.