Noémie Elhadad, PhD

  • Chair, Department of Biomedical Informatics
  • Associate Professor of Biomedical Informatics

On the web

Profile Headshot

Profile Navigation

Overview

Dr. Noémie Elhadad is Chair and Associate Professor of Biomedical Informatics of the Department of Biomedical Informatics, and she is affiliated with Computer Science and the Data Science Institute at Columbia University. She obtained her PhD in 2006 in Computer Science, 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 starting in 2006 before joining the Department of Biomedical Informatics at Columbia in 2007. Dr. Elhadad was Chair of the Health Analytics Center at the Columbia Data Science Institute from 2013 to 2016.

Academic Appointments

  • Chair, Department of Biomedical Informatics
  • Associate Professor of Biomedical Informatics

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