F A Khan
Dados Biográficos
| ID | 15249 |
|---|---|
| NOME | F A Khan |
| PRENOMES | F A |
| SOBRENOME | Khan |
| ASSINATURA | KHAN F A |
| AFILIAÇÕES | McGill University Health Centre |
| ORCID | 0000-0003-0473-8734 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 4 |
| TOTAL DE CITAÇÕES | 2 |
| OBRAS COMO AUTOR | 4 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 1967 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2024 |
| ÍNDICE H | 1 |
Self-tests for Covid-19
COVID-19 self-testing strategy (COVIDST) can rapidly identify symptomatic and asymptomatic SARS-CoV-2-infected individuals and their contacts, potentially reducing transmission. In this living systematic review, we evaluated the evidence for real-world COVIDST performance. Two independent reviewers searched six databases (PubMed, Embase, Web of Science, World Health Organization database, Cochrane COVID-19 registry, Europe PMC) for the period Apr…
Respiratory health and its determinants among Nunavimmiut
Smoking and food insecurity are both highly prevalent and strongly associated with respiratory symptoms in Nunavik. Together with essential smoking prevention and cessation programs, our findings suggest that solving food security and housing crises, improving socioeconomic conditions, and promoting traditional lifestyle may improve respiratory health in Nunavik
Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis
Fouilles d'Amri
Fouilles d'Amri
Respiratory health and its determinants among Nunavimmiut
Smoking and food insecurity are both highly prevalent and strongly associated with respiratory symptoms in Nunavik. Together with essential smoking prevention and cessation programs, our findings suggest that solving food security and housing crises, improving socioeconomic conditions, and promoting traditional lifestyle may improve respiratory health in Nunavik
Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis
Self-tests for Covid-19
COVID-19 self-testing strategy (COVIDST) can rapidly identify symptomatic and asymptomatic SARS-CoV-2-infected individuals and their contacts, potentially reducing transmission. In this living systematic review, we evaluated the evidence for real-world COVIDST performance. Two independent reviewers searched six databases (PubMed, Embase, Web of Science, World Health Organization database, Cochrane COVID-19 registry, Europe PMC) for the period Apr…
Medicine (3 obras) · Biology (2 obras) · COVID-19 diagnosis using AI (2 obras) · Internal Medicine (2 obras) · 2019-20 coronavirus outbreak (1 obra) · Airway (1 obra) · Airway obstruction (1 obra) · Ancient history (1 obra) · Ancient Near East History (1 obra) · Artificial Intelligence (1 obra)