World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
54
Citations
12416
World Ranking
4534
National Ranking
2126

Overview

Noémie Elhadad is affiliated with Columbia University in the United States. Their research spans across the field of Medicine with a specific focus on multiple subfields and topics.

The main subfields of study include:

  • Artificial Intelligence
  • Reproductive Medicine
  • General Health Professions
  • Public Health, Environmental and Occupational Health
  • Epidemiology

Key research topics covered in their work are:

  • Endometriosis Research and Treatment
  • Topic Modeling
  • Biomedical Text Mining and Ontologies
  • Machine Learning in Healthcare
  • Natural Language Processing Techniques
  • Uterine Myomas and Treatments
  • Artificial Intelligence in Healthcare and Education

Noémie Elhadad has contributed frequently to several publication venues, including:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Journal of the American Medical Informatics Association
  • JAMA Network Open
  • npj Digital Medicine

Recent papers by Noémie Elhadad include:

  • Characterizing physiological and symptomatic variation in menstrual cycles using self-tracked mobile-health data, 2020, npj Digital Medicine
  • Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data, 2020, Applied Clinical Informatics
  • Clinician involvement in research on machine learning-based predictive clinical decision support for the hospital setting: A scoping review, 2020, Journal of the American Medical Informatics Association
  • The messiness of the menstruator: assessing personas and functionalities of menstrual tracking apps, 2021, Journal of the American Medical Informatics Association
  • Deep-learning approaches to identify critically Ill patients at emergency department triage using limited information, 2020, Journal of the American College of Emergency Physicians Open

Frequent coauthors working with Noémie Elhadad include:

  • Harry Reyes Nieva
  • Jason Zucker
  • Suzanne Bakken
  • Ipek Ensari
  • Tony Sun

Best Publications

  • Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission

    Rich Caruana;Yin Lou;Johannes Gehrke;Paul Koch

  • An Unsupervised Aspect-Sentiment Model for Online Reviews

    Samuel Brody;Noemie Elhadad

  • A review of approaches to identifying patient phenotype cohorts using electronic health records

    Chaitanya P. Shivade;Preethi Raghavan;Eric Fosler-Lussier;Peter J. Embi

  • Beyond the Stars: Improving Rating Predictions using Review Text Content.

    Gayatree Ganu;Noemie Elhadad;Amélie Marian

  • Inferring strategies for sentence ordering in multidocument news summarization

    Regina Barzilay;Noemie Elhadad;Kathleen R. McKeown

  • Indicators of retention in remote digital health studies: a cross-study evaluation of 100,000 participants

    Abhishek Pratap;Abhishek Pratap;Elias Chaibub Neto;Phil Snyder;Carl Stepnowsky

  • Overview of the ShARe/CLEF eHealth Evaluation Lab 2013

    Hanna Suominen;Sanna Salanterä;Sumithra Velupillai;Wendy W. Chapman

  • A Comparison of Features for Automatic Readability Assessment

    Lijun Feng;Martin Jansche;Matt Huenerfauth;Noémie Elhadad

  • Unsupervised biomedical named entity recognition

    Shaodian Zhang;Noémie Elhadad

  • Diagnosis code assignment: models and evaluation metrics

    Adler J. Perotte;Rimma Pivovarov;Karthik Natarajan;Karthik Natarajan;Nicole Gray Weiskopf

  • Sentence alignment for monolingual comparable corpora

    Regina Barzilay;Noemie Elhadad

  • Automated methods for the summarization of electronic health records

    Rimma Pivovarov;Noémie Elhadad

  • Putting it Simply: a Context-Aware Approach to Lexical Simplification

    Or Biran;Samuel Brody;Noemie Elhadad

  • SemEval-2015 Task 14: Analysis of Clinical Text

    Noémie Elhadad;Sameer Pradhan;Sharon Gorman;Suresh Manandhar

  • Cognitively Motivated Features for Readability Assessment

    Lijun Feng;Noémie Elhadad;Matt Huenerfauth

  • Hierarchically Supervised Latent Dirichlet Allocation

    Adler J. Perotte;Frank Wood;Noemie Elhadad;Nicholas Bartlett

  • Evaluating the state of the art in disorder recognition and normalization of the clinical narrative.

    Sameer Pradhan;Noémie Elhadad;Brett R. South;David Martínez

  • HARVEST, a longitudinal patient record summarizer

    Jamie S. Hirsch;Jessica S. Tanenbaum;Sharon Lipsky Gorman;Connie Liu

  • Multi-Label Classification of Patient Notes a Case Study on ICD Code Assignment

    Tal Baumel;Jumana Nassour-Kassis;Raphael Cohen;Michael Elhadad

  • Learning probabilistic phenotypes from heterogeneous EHR data

    Rimma Pivovarov;Adler J. Perotte;Edouard Grave;John Angiolillo

Frequent Co-Authors

Wendy W. Chapman
Wendy W. Chapman University of Melbourne
Guergana Savova
Guergana Savova Harvard University
Sameer Pradhan
Sameer Pradhan Vassar College
George Hripcsak
George Hripcsak Columbia University
Kathleen R. McKeown
Kathleen R. McKeown Columbia University
Edouard Grave
Edouard Grave Facebook (United States)
Rajesh Ranganath
Rajesh Ranganath New York University
Suzanne Bakken
Suzanne Bakken Columbia University
David M. Blei
David M. Blei Columbia University

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