ArticleCanada communicable disease report = Releve des maladies transmissibles au Canada2020
Application of artificial intelligence to the
Article in Canada communicable disease report = Releve des maladies transmissibles au Canada, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Meat-Borne Bacterial Pathogen Detection: Conventional, Molecular and Emerging AI-Based Strategies.Diagnostics (Basel, Switzerland) · 2026Review
- Leveraging artificial intelligence for One Health: opportunities and challenges in tackling antimicrobial resistance - scoping review.One health outlook · 2025Article
- Proteome mining of Yersinia Enterocolitica for drug targets and computational inhibitor identification with ADMET, anti-inflammation potential and formulation characteristics.BioData mining · 2025Article
- Using random forest to predict antimicrobial minimum inhibitory concentrations of nontyphoidal Salmonella in Taiwan.Veterinary research · 2023Article
- Application of Artificial Intelligence in Combating High Antimicrobial Resistance Rates.Antibiotics (Basel, Switzerland) · 2022Review
- Mechanism of drug resistance in bacteria: efflux pump modulation for designing of new antibiotic enhancers.Folia microbiologica · 2021Review
- Surveillance of antimicrobial resistance in low- and middle-income countries: a scattered picture.Antimicrobial resistance and infection control · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Each year, approximately one in eight Canadians are affected by foodborne illness, either through outbreaks or sporadic illness, with animals being the major reservoir for the pathogens. Whole genome sequence analyses are now routinely implemented by public and animal health laboratories to define epidemiological disease clusters and to identify potential sources of infection. Similarly, a number of bioinformatics tools can be used to identify virulence and antimicrobial resistance (AMR) determinants in the genomes of pathogenic strains. Many important clinical and phenotypic characteristics of these pathogens can now be predicted using machine learning algorithms applied to whole genome sequence data. In this overview, we compare the ability of support vector machines, gradient-boosted decision trees and artificial neural networks to predict the levels of AMR within
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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.