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Matthias I Gröschel

Biographic Data

ID9068681
NAMEMatthias I Gröschel
GIVEN NAMESMatthias I
FAMILY NAMEGröschel
SIGNATUREGRÖSCHEL M I
AFFILIATIONSHarvard University
ORCID0000-0002-2509-3034
VERIFIEDYes
TOTAL WORKS2
TOTAL CITATIONS0
WORKS AS AUTHOR2
WORKS AS EDITOR0
FIRST PUBLICATION YEAR2023
LATEST PUBLICATION YEAR2024
H-INDEX0
  • Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning

    Open Access•Avika Dixit, Luca Freschi et al.•ARTICLE•BMJ Global Health•2024

    BACKGROUND: ) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction. METHODS: WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa. RESULT…

  • Rethinking global digital health and AI-for-health innovation challenges

    Open Access•Andrew Farlow, Alexander Hoffmann et al.•ARTICLE•PLOS Global Public Health•2023

    Digital health technologies can help tackle challenges in global public health. Digital and AI-for-Health Challenges, controlled events whose goal is to generate solutions to a given problem in a defined period of time, are one way of catalysing innovation. This article proposes an expanded investment framework for Global Health AI and digitalhealth Innovation that goes beyond traditional factors such as return on investment. Instead, we propose …

No prominent works on this page.

  • Rethinking global digital health and AI-for-health innovation challenges

    Open Access•Andrew Farlow, Alexander Hoffmann et al.•ARTICLE•PLOS Global Public Health•2023

    Digital health technologies can help tackle challenges in global public health. Digital and AI-for-Health Challenges, controlled events whose goal is to generate solutions to a given problem in a defined period of time, are one way of catalysing innovation. This article proposes an expanded investment framework for Global Health AI and digitalhealth Innovation that goes beyond traditional factors such as return on investment. Instead, we propose …

  • Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning

    Open Access•Avika Dixit, Luca Freschi et al.•ARTICLE•BMJ Global Health•2024

    BACKGROUND: ) whole-genome sequencing (WGS) data, to generate expanded country-level resistance prevalence estimates (antibiograms) using in silico resistance prediction. METHODS: WGS data. We used a validated random forest model to predict phenotypic resistance to 12 drugs and bias-corrected for model performance, outbreak sampling and rifampicin resistance oversampling. Validation leveraged a national DR survey conducted in South Africa. RESULT…

Medicine (2 works) · Antibiotic resistance (1 work) · Antibiotic Use and Resistance (1 work) · Antibiotics (1 work) · Artificial Intelligence in Healthcare and Education (1 work) · Biology (1 work) · Biomedical and Engineering Education (1 work) · Business (1 work) · Computer Science (1 work) · Digital health (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