ReviewHeliyon2022
Predictive models for COVID-19 detection using routine blood tests and machine learning.
Review in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Early biomarkers in hospitalized patients as predictors of post-acute sequelae of SARS-CoV-2 infection: a one-year cohort study.BMC infectious diseases · 2025Article
- Validation and interpretation of machine-learning models for rapid identification of active tuberculosis infection using routine laboratory indicators.Frontiers in cellular and infection microbiology · 2025Article
- Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis.F1000Research · 2024Article
- Association of triglyceride/high-density lipoprotein cholesterol ratio with severe complications of COVID-19.Heliyon · 2023Article
- Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence in healthcare: combining deep learning and Bayesian optimization to forecast COVID-19 confirmed cases.Frontiers in artificial intelligence · 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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient's state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning.
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.