World's Best Scientists 2026 revealed!

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Computer Science

D-Index
72
Citations
28273
World Ranking
1642
National Ranking
846

Overview

Nigam H. Shah is affiliated with Stanford University in the United States, with a research focus primarily in the field of Medicine. Their work spans several important subfields, including Artificial Intelligence, Health Informatics, Infectious Diseases, Health Information Management, and Economics and Econometrics.

Their research topics cover a diverse range of areas within healthcare and medical informatics, notably:

  • Machine Learning in Healthcare
  • Artificial Intelligence in Healthcare and Education
  • Health Systems, Economic Evaluations, Quality of Life
  • COVID-19 Clinical Research Studies
  • Electronic Health Records Systems
  • Artificial Intelligence in Healthcare
  • Healthcare cost, quality, practices

The scientist has contributed to various publication venues, frequently publishing in:

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

Some of their recent papers include:

  • "Rates of Co-infection Between SARS-CoV-2 and Other Respiratory Pathogens," 2020, JAMA
  • "Defining the features and duration of antibody responses to SARS-CoV-2 infection associated with disease severity and outcome," 2020, Science Immunology
  • "Creation and Adoption of Large Language Models in Medicine," 2023, JAMA
  • "MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care," 2020, Journal of the American Medical Informatics Association
  • "Toward expert-level medical question answering with large language models," 2025, Nature Medicine

The scientist's work involves collaboration with several frequent co-authors, including:

  • Jason Fries
  • Jose Posada
  • Ethan Steinberg
  • Patrick Ryan
  • Alison Callahan

Best Publications

  • The OBO Foundry : coordinated evolution of ontologies to support biomedical data integration

    Barry Smith;Michael Ashburner;Cornelius Rosse;Jonathan Bard

  • Scalable and accurate deep learning with electronic health records

    Alvin Rajkomar;Alvin Rajkomar;Eyal Oren;Kai Chen;Andrew M. Dai

  • Scalable and accurate deep learning for electronic health records

    Alvin Rajkomar;Eyal Oren;Kai Chen;Andrew M. Dai

  • Implementing Machine Learning in Health Care - Addressing Ethical Challenges.

    Danton S. Char;Nigam H. Shah;David Magnus

  • Observational Health Data Sciences and Informatics (OHDSI): Opportunities for Observational Researchers

    George Hripcsak;Jon D. Duke;Nigam H. Shah;Christian G. Reich

  • BioPortal: ontologies and integrated data resources at the click of a mouse

    Natalya Fridman Noy;Nigam H. Shah;Patricia L. Whetzel;Benjamin Dai

  • BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications

    Patricia L. Whetzel;Natalya Fridman Noy;Nigam H. Shah;Paul R. Alexander

  • The BioPAX community standard for pathway data sharing

    Emek Demir;Emek Demir;Michael P. Cary;Suzanne Paley;Ken Fukuda

  • Improving palliative care with deep learning

    Anand Avati;Kenneth Jung;Stephanie Harman;Lance Downing

  • Novel data-mining methodologies for adverse drug event discovery and analysis.

    Rave Harpaz;William DuMouchel;William DuMouchel;Nigam H. Shah;David Madigan;David Madigan

  • What This Computer Needs Is a Physician: Humanism and Artificial Intelligence.

    Abraham Verghese;Nigam H. Shah;Robert A. Harrington

  • Biomedical ontologies: a functional perspective

    Daniel L. Rubin;Nigam Shah;Natalya Fridman Noy

  • Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system.

    Rave Harpaz;William DuMouchel;Paea LePendu;Anna Bauer-Mehren

  • MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care.

    Tina Hernandez-Boussard;Selen Bozkurt;John P A Ioannidis;Nigam H Shah

  • The open biomedical annotator.

    Clement Jonquet;Nigam H Shah;Mark A Musen

  • The National Center for Biomedical Ontology

    Mark A. Musen;Natalya F. Noy;Nigam H. Shah;Patricia L. Whetzel

  • Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

    Rave Harpaz;Alison Callahan;Suzanne Tamang;Yen Low

  • Web-scale pharmacovigilance: listening to signals from the crowd

    Ryen W White;Nicholas P Tatonetti;Nigam H Shah;Russ B Altman

  • A Nondegenerate Code of Deleterious Variants in Mendelian Loci Contributes to Complex Disease Risk

    David R. Blair;Christopher S. Lyttle;Jonathan M. Mortensen;Charles F. Bearden

  • Making Machine Learning Models Clinically Useful

    Nigam H. Shah;Arnold Milstein;Steven C. Bagley

  • BioPortal: Ontologies and Integrated Data Resources at the Click of a Mouse

    Patricia L. Whetzel;Nigam H. Shah;Natalya F. Noy;Benjamin Dai

  • Biomedical ontologies: a functional

    L. Rubin;Nigam H. Shah;Natalya F. Noy

Frequent Co-Authors

Mark A. Musen
Mark A. Musen Stanford University
George Hripcsak
George Hripcsak Columbia University
Natalya F. Noy
Natalya F. Noy Google (United States)
Marc A. Suchard
Marc A. Suchard University of California, Los Angeles
Michel Dumontier
Michel Dumontier Maastricht University
William DuMouchel
William DuMouchel Oracle (United States)
Daniel L. Rubin
Daniel L. Rubin Stanford University
Benjamin A. Pinsky
Benjamin A. Pinsky Stanford University
Nina V. Fedoroff
Nina V. Fedoroff Pennsylvania State University
Sean D. Mooney
Sean D. Mooney University of Washington

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