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INFORMATION FOR

    Samah Fodeh, PhD

    Associate Professor of Emergency Medicine & Biostatistics (Health Informatics)

    About

    Titles

    Associate Professor of Emergency Medicine & Biostatistics (Health Informatics)

    Biography

    Samah Fodeh-Jarad, PhD is an Associate Professor in the Department of Emergency Medicine, with a secondary appointment in the Yale School of Public Health. She is also affiliated with the Yale Institute for Global Health (YIHG) and the VA Connecticut Healthcare System. Dr. Fodeh has distinguished herself as researcher in the field of Biomedical Informatics and Big Data Science with a growing national and international reputation. Her contributions include the development of complex computational methods and tools that are critical for advancing biomedical informatics research and data science. Through her work, Dr. Fodeh demonstrates the utility of exploiting and combining multiple data modalities by employing methods from data mining, machine learning, deep learning, and natural language processing. Her research is focused on health and social media data mining to answer critical health related questions to suicide risk, opioid addiction, migraine diagnosis and treatment. Dr. Fodeh is also interested in studying patient-centered aspects of care including communications in clinical settings between patients and healthcare providers, stigmatizing language, social determinants of health and their impact on health-related outcomes.

    Appointments

    Education & Training

    PhD
    Michigan State University (2010)

    Research

    Overview

    Medical Research Interests

    Data Mining; Machine Learning; Medical Informatics; Natural Language Processing; Social Behavior Disorders

    Public Health Interests

    COVID-19

    Research at a Glance

    Yale Co-Authors

    Frequent collaborators of Samah Fodeh's published research.

    Publications

    2022

    2021

    2020

    Academic Achievements & Community Involvement

    • activity

      Utilizing Machine Learning for Patient Phenotyping

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