Skip to main content

ETHNOS_APP

Home • Search • Journals • List 0

Amin Golabpour

Biographic Data

ID7754782
NAMEAmin Golabpour
GIVEN NAMESAmin
FAMILY NAMEGolabpour
SIGNATUREGOLABPOUR A
AFFILIATIONSShahroud University of Medical Sciences
ORCID0000-0001-7649-4033
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
AUTHOR COUNT2
EDITOR COUNT0
FIRST PUBLICATION YEAR2021
LATEST PUBLICATION YEAR2021
H-INDEX0
  • Spatial-time analysis of cardiovascular emergency medical requests

    Open Access•Ali Azimi, Nazgol Bagheri et al.•ARTICLE•BMC Public Health•2021

    The use of geographical information systems and spatial analyses in modelling and quantifying EMS response time provides a new vein of knowledge for decision makers in emergency services management. Spatial as well as temporal clustering of EMS calls were present in the study area. The reasons for clustering of unfavorable time indices for EMS response requires further exploration. This approach enables policymakers to design tailored interventio…

  • Predicting the incidence of Covid-19 using data mining

    Open Access•Fatemeh Ahouz, Amin Golabpour•ARTICLE•BMC Public Health•2021

    Using data from different geographical regions within a country and discovering the pattern of prevalence in a region and its neighboring areas, our boosting-based model was able to accurately predict the incidence of COVID-19 within a two-week period

No prominent works on this page.

  • Spatial-time analysis of cardiovascular emergency medical requests

    Open Access•Ali Azimi, Nazgol Bagheri et al.•ARTICLE•BMC Public Health•2021

    The use of geographical information systems and spatial analyses in modelling and quantifying EMS response time provides a new vein of knowledge for decision makers in emergency services management. Spatial as well as temporal clustering of EMS calls were present in the study area. The reasons for clustering of unfavorable time indices for EMS response requires further exploration. This approach enables policymakers to design tailored interventio…

  • Predicting the incidence of Covid-19 using data mining

    Open Access•Fatemeh Ahouz, Amin Golabpour•ARTICLE•BMC Public Health•2021

    Using data from different geographical regions within a country and discovering the pattern of prevalence in a region and its neighboring areas, our boosting-based model was able to accurately predict the incidence of COVID-19 within a two-week period

Biostatistics (2 works) · Demography (2 works) · Demography (2 works) · Epidemiology (2 works) · Medicine (2 works) · Anomaly Detection Techniques and Applications (1 work) · Arrival time (1 work) · Cartography (1 work) · Coronavirus disease 2019 (COVID-19 (1 work) · COVID-19 diagnosis using AI (1 work)

Ethnos_APP • Open Source Project • MIT License • Frontend v2.0.0 • Privacy and Cookies • API Documentation: api.ethnos.app/docs • API Source Code: GitHub • DOI: 10.5281/zenodo.17049435 • Frontend Source Code: GitHub • DOI: 10.5281/zenodo.17050053 • cruz.rio.br • Expectantes Misericordiae