Maha R Farhat
Dados Biográficos
| ID | 9068680 |
|---|---|
| NOME | Maha R Farhat |
| PRENOMES | Maha R |
| SOBRENOME | Farhat |
| ASSINATURA | FARHAT M R |
| AFILIAÇÕES | Harvard Medical School |
| VERIFICADO | Não |
| TOTAL DE OBRAS | 2 |
| TOTAL DE CITAÇÕES | 0 |
| OBRAS COMO AUTOR | 2 |
| OBRAS COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2023 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2024 |
| ÍNDICE H | 0 |
Estimation of country-specific tuberculosis resistance antibiograms using pathogen genomics and machine learning
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
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 …
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Rethinking global digital health and AI-for-health innovation challenges
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
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 obras) · Antibiotic resistance (1 obra) · Antibiotic Use and Resistance (1 obra) · Antibiotics (1 obra) · Artificial Intelligence in Healthcare and Education (1 obra) · Biology (1 obra) · Biomedical and Engineering Education (1 obra) · Business (1 obra) · Computer Science (1 obra) · Digital health (1 obra)