Assistant Professor of Biomedical Informatics
Harvard Medical School
Dr. Farhat is an Assistant Professor of of
Biomedical Informatics atHarvard Medical School and a a practicing physician at
the Massachusetts General Hospital Division of Pulmonary and Critical Care
Medicine.
Dr. Farhat's research focuses on the development and application of methods for associating genotype and phenotype in infectious disease pathogens, with a strong emphasis on translation to better diagnostics and surveillance in resource-poor settings. Farhat's work has focused on bacterial and viral pathoges and spans the spectrum from computational analysis to field studies. She is PI and Co-Investigator on several large projects funded by NIH including the NIAID and the BD2K initiative.
Maha Farhat holds an MD from the McGill University Faculty of Medicine and a MSc in biostatistics from the Harvard Chan School of Public Health.
Assistant Professor of Biomedical Informatics
Harvard Medical School
Dr. Farhat is an Assistant Professor of of
Biomedical Informatics atHarvard Medical School and a a practicing physician at
the Massachusetts General Hospital Division of Pulmonary and Critical Care
Medicine.
Dr. Farhat's research focuses on the development and application of methods for associating genotype and phenotype in infectious disease pathogens, with a strong emphasis on translation to better diagnostics and surveillance in resource-poor settings. Farhat's work has focused on bacterial and viral pathoges and spans the spectrum from computational analysis to field studies. She is PI and Co-Investigator on several large projects funded by NIH including the NIAID and the BD2K initiative.
Maha Farhat holds an MD from the McGill University Faculty of Medicine and a MSc in biostatistics from the Harvard Chan School of Public Health.
Journal article
Rapid genotype-based drug susceptibility testing for the Mycobacterium tuberculosis complex (MTBC) relies on a comprehensive knowledgebase of the genetic determinants of resistance. Here we present a catalogue of resistance-associated mutations using a regression-based approach and benchmark it against the 2nd edition of the World Health Organisation (WHO) mutation catalogue. We train multivariate logistic regression models on over 52,000 MTBC isolates to associate binary resistance phenotypes...
Journal article
CONCLUSIONS: Half of participants on TB treatment experienced ADRs, but most remained adherent to treatment. Among participants with moderate or severe ADRs, those with poorly controlled HIV, alcohol use, or smoked substance use had lower adherence.