ArticleHealthcare (Basel, Switzerland)2022
Adverse Effects of COVID-19 Vaccination: Machine Learning and Statistical Approach to Identify and Classify Incidences of Morbidity and Postvaccination Reactogenicity.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Lymphadenopathy post-COVID-19 vaccination with increased FDG uptake may be falsely attributed to oncological disorders: A systematic review.Journal of medical virology · 2022Pooled it
- A data-driven machine learning framework to predict side effects of AstraZeneca and sinopharm COVID-19 vaccines.Scientific reports · 2025Article
- Experience and side effects of COVID-19 vaccine uptake among university students: a cross-sectional survey study.Frontiers in public health · 2024Article
- Computational design and evaluation of mRNA- and protein-based conjugate vaccines for influenza A and SARS-CoV-2 viruses.Journal, genetic engineering & biotechnology · 2023Article
- Review
- Article
- Heart Rate Variability in Subjects with Severe Allergic Background Undergoing COVID-19 Vaccination.Vaccines · 2023Article
- COVID-19 vaccinations and their side effects: a scoping systematic review.F1000Research · 2023Article
- Vaccine hesitancy in the post-vaccination COVID-19 era: a machine learning and statistical analysis driven study.Evolutionary intelligence · 2023Article
- Sex-disaggregated outcomes of adverse events after COVID-19 vaccination: A Dutch cohort study and review of the literature.Frontiers in immunology · 2023Review
- Covid-19 vaccination reported side effects and hesitancy among the Syrian population: a cross-sectional study.Annals of medicine · 2023Article
- Side effects of COVID-19 vaccines in the middle eastern population.Frontiers in immunology · 2023Review
- Quantitative ultrasound image analysis of axillary lymph nodes to differentiate malignancy from reactive benign changes due to COVID-19 vaccination.European journal of radiology · 2022Article
- Modeling COVID-19 Vaccine Adverse Effects with a Visualized Knowledge Graph Database.Healthcare (Basel, Switzerland) · 2022Article
- Article
- COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.Informatics in medicine unlocked · 2022Article
- Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19.Diagnostics (Basel, Switzerland) · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Good vaccine safety and reliability are essential for successfully countering infectious disease spread. A small but significant number of adverse reactions to COVID-19 vaccines have been reported. Here, we aim to identify possible common factors in such adverse reactions to enable strategies that reduce the incidence of such reactions by using patient data to classify and characterise those at risk. We examined patient medical histories and data documenting postvaccination effects and outcomes. The data analyses were conducted using a range of statistical approaches followed by a series of machine learning classification algorithms. In most cases, a group of similar features was significantly associated with poor patient reactions. These included patient prior illnesses, admission to hospitals and SARS-CoV-2 reinfection. The analyses indicated that patient age, gender, taking other medications, type-2 diabetes, hypertension, allergic history and heart disease are the most significant pre-existing factors associated with the risk of poor outcome. In addition, long duration of hospital treatments, dyspnoea, various kinds of pain, headache, cough, asthenia, and physical disability were the most significant clinical predictors. The machine learning classifiers that are trained with medical history were also able to predict patients with complication-free vaccination and have an accuracy score above 90%. Our study identifies profiles of individuals that may need extra monitoring and care (e.g., vaccination at a location with access to comprehensive clinical support) to reduce negative outcomes through classification approaches.
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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.