Noémie Elhadad, PhD

  • Associate Professor of Biomedical Informatics

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Overview

Dr. Noémie Elhadad is the chair of the Department of Biomedical Informatics and the director of the AI at VP&S Initiative at Columbia University Irving Medical Center. Dr. Elhadad is an associate professor of biomedical Informatics and is affiliated with the Department of Computer Science and the Columbia University Data Science Institute.

Dr. Elhadad’s research lies at the intersection of machine learning, natural language processing, and medicine. She investigates ways in which observational clinical data (e.g., electronic health records) and patient-generated data (e.g., online health community discussions, mobile health data) can enhance access to relevant information for clinicians, patients, and health researchers alike and can ultimately impact healthcare and health of patients.

Dr. Elhadad obtained her PhD in Computer Science from Columbia University, focusing on multi-document, patient-specific text summarization of the clinical literature. She was on the computer science faculty at The City College of New York and the CUNY graduate center before joining the Department of Biomedical Informatics at Columbia in 2007. Dr. Elhadad served as chair of the Health Analytics Center at the Columbia Data Science Institute from 2013 to 2016.


Academic Appointments

  • Associate Professor of Biomedical Informatics

Administrative Titles

  • Chair, Department of Biomedical Informatics
  • Director, AI at VP&S Initiative

Credentials & Experience

Education & Training

  • PhD, 2006 Computer Science, Columbia University

Research

Dr. Elhadad’s research interests are at the intersection of machine learning, natural language processing, and medicine. She investigates ways in which observational clinical data (e.g., electronic health records) and patient-generated data (e.g., online health community discussions, mobile health data) can enhance access to relevant information for clinicians, patients, and health researchers alike and can ultimately impact healthcare and health of patients.

Research Interests

  • Artificial Intelligence (AI)
  • Bioinformatics
  • Machine Learning (ML)
  • Women's Health

Selected Publications

  1. Joshi S, Urteaga I, van Amsterdam WAC, Hripcsak G, Elias P, Recht B, Elhadad N, Fackler J, Sendak MP, Wiens J, Deshpande K, Wald Y, Fiterau M, Lipton Z, Malinsky D, Nayan M, Namkoong H, Park S, Vogt JE, Ranganath R. AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. J Am Med Inform Assoc. 2025 Mar 1;32(3):589-594. doi: 10.1093/jamia/ocae301. PMID: 39775871.
  2. Adams G, Fabbri AR, Ladhak F, Lehman E, Elhadad N. From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting. Proc Conf Empir Methods Nat Lang Process. 2023 Dec;2023(4th New Frontier Summarization Workshop):68-74. doi: 10.18653/v1/2023.newsum-1.7. PMID: 39315281; PMCID: PMC11419567.
  3. Elhussein A, Baymuradov U; NYGC ALS Consortium; Elhadad N, Natarajan K, Gürsoy G. A framework for sharing of clinical and genetic data for precision medicine applications. Nat Med. 2024 Dec;30(12):3578-3589. doi: 10.1038/s41591-024-03239-5. Epub 2024 Sep 3. PMID: 39227443; PMCID: PMC11645287.
  4. Bear Don't Walk Iv OJ, Pichon A, Nieva HR, Sun T, Altosaar J, Natarajan K, Perotte A, Tarczy-Hornoch P, Demner-Fushman D, Elhadad N. Auditing Learned Associations in Deep Learning Approaches to Extract Race and Ethnicity from Clinical Text. AMIA Annu Symp Proc. 2024 Jan 11;2023:289-298. PMID: 38222422; PMCID: PMC10785932.

For a complete list of publications, please visit PubMed.gov