World's Best Scientists 2026 revealed!

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Engineering and Technology

D-Index
35
Citations
7810
World Ranking
8880
National Ranking
2469

Research.com Recognitions

  • 1981 - Fellow of the American Statistical Association (ASA)

Overview

William DuMouchel is affiliated with Oracle in the United States and has contributed primarily to research in pharmacology, toxicology, pharmaceutics, and medicine. Their work spans computational drug discovery methods, pharmacogenetics and drug metabolism, receptor mechanisms and signaling, as well as pharmacovigilance and adverse drug reactions.

The scientist has co-authored papers with several frequent collaborators, including Robert Ietswaart, Seda Arat, Amanda X. Chen, Saman Farahmand, and Bumjun Kim.

William DuMouchel's publications have appeared in the following venues:

  • EBioMedicine
  • Drug Safety

Two recent papers reflect the focus of their research:

  • Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology, 2020, EBioMedicine
  • Signaling COVID-19 Vaccine Adverse Events, 2022, Drug Safety

The main fields of study for this researcher include:

  • Pharmacology, Toxicology and Pharmaceutics
  • Medicine

Their work spans several subfields, such as:

  • Computational Theory and Mathematics
  • Pharmacology
  • Molecular Biology
  • Infectious Diseases
  • Toxicology

Key research topics covered by William DuMouchel include:

  • Computational Drug Discovery Methods
  • Pharmacogenetics and Drug Metabolism
  • Receptor Mechanisms and Signaling
  • SARS-CoV-2 and COVID-19 Research
  • Pharmacovigilance and Adverse Drug Reactions
  • Heparin-Induced Thrombocytopenia and Thrombosis

They were recognized as a Fellow of the American Statistical Association (ASA) in 1981.

Best Publications

  • Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System

    William Dumouchel

  • A Meta-analysis of 16 Randomized Controlled Trials to Evaluate Computer-Based Clinical Reminder Systems for Preventive Care in the Ambulatory Setting

    Steven Shea;William DuMouchel;Lisa Bahamonde

  • Computer Intrusion: Detecting Masquerades

    Matthias Schonlau;William DuMouchel;Wen-Hua Ju;Alan F. Karr

  • Unlocking Clinical Data from Narrative Reports: A Study of Natural Language Processing

    George Hripcsak;Carol Friedman;Philip O. Alderson;William DuMouchel

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

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

  • The New Jersey Data Reduction Report.

    Daniel Barbará;William DuMouchel;Christos Faloutsos;Peter J. Haas

  • Empirical bayes screening for multi-item associations

    William DuMouchel;Daryl Pregibon

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

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

  • Interpreting observational studies: why empirical calibration is needed to correct p‐values

    Martijn J. Schuemie;Patrick B. Ryan;William DuMouchel;William DuMouchel;Marc A. Suchard;Marc A. Suchard

  • A simple Bayesian modification of D-optimal designs to reduce dependence on an assumed model

    Unknown

  • Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions.

    Rave Harpaz;Rave Harpaz;Santiago Vilar;William DuMouchel;Hojjat Salmasian

  • Evaluating the Impact of Database Heterogeneity on Observational Study Results

    David Madigan;Patrick B. Ryan;Martijn Schuemie;Paul E. Stang

  • Natural language processing in an operational clinical information system

    Carol Friedman;George Hripcsak;William DuMouchel;Stephen B. Johnson

  • Squashing flat files flatter

    William DuMouchel;Chris Volinsky;Theodore Johnson;Corinna Cortes

  • Disproportionality analysis using empirical Bayes data mining: a tool for the evaluation of drug interactions in the post-marketing setting

    June S. Almenoff;June S. Almenoff;William DuMouchel;L. Allen Kindman;Xionghu Yang

  • Comparative performance of two quantitative safety signalling methods: implications for use in a pharmacovigilance department.

    June S. Almenoff;Karol K. LaCroix;Nancy A. Yuen;David Fram

  • Toward enhanced pharmacovigilance using patient-generated data on the internet.

    Ryen W. White;Rave Harpaz;Nigam H. Shah;William DuMouchel;William DuMouchel

  • Likelihood-Based Data Squashing: A Modeling Approach to Instance Construction

    David Madigan;Nandini Raghavan;William Dumouchel;Martha Nason

  • A comparison of the empirical performance of methods for a risk identification system

    Patrick B. Ryan;Patrick B. Ryan;Paul E. Stang;Paul E. Stang;J. Marc Overhage;J. Marc Overhage;Marc A. Suchard;Marc A. Suchard

  • Computer-generated Informational Messages Directed to Physicians: Effect on Length of Hospital Stay

    Steven Shea;Robert V. Sideli;William DuMouchel;Gerald Pulver

  • Medical decision support: experience with implementing the Arden Syntax at the Columbia-Presbyterian Medical Center.

    R. A. Jenders;G. Hripcsak;R. V. Sideli;W. DuMouchel

  • Adaptation of Bayesian Data Mining Algorithms to Longitudinal Claims Data: Coxib Safety as an Example

    Jeffrey R. Curtis;Hong Cheng;Elizabeth Delzell;David Fram

  • A fast computer intrusion detection algorithm based on hypothesis testing of command transition probabilities

    William DuMouchel;Matthias Schonlau

  • Antipsychotics, glycemic disorders, and life-threatening diabetic events: a Bayesian data-mining analysis of the FDA adverse event reporting system (1968-2004).

    William DuMouchel;David Fram;Xionghu Yang;Ramy A. Mahmoud

  • Machine learning guided association of adverse drug reactions with in vitro target-based pharmacology

    Robert Ietswaart;Seda Arat;Amanda X. Chen;Saman Farahmand

  • A time-indexed reference standard of adverse drug reactions

    Rave Harpaz;David Odgers;Greg Gaskin;William DuMouchel

  • Evaluation of Disproportionality Safety Signaling Applied to Healthcare Databases

    William DuMouchel;William DuMouchel;Patrick B. Ryan;Patrick B. Ryan;Martijn J. Schuemie;Martijn J. Schuemie;David Madigan;David Madigan

Frequent Co-Authors

David Madigan
David Madigan Northeastern University
Nigam H. Shah
Nigam H. Shah Stanford University
Martijn J. Schuemie
Martijn J. Schuemie Janssen (Belgium)
Carol Friedman
Carol Friedman Columbia University
Marc A. Suchard
Marc A. Suchard University of California, Los Angeles
Eric Horvitz
Eric Horvitz Microsoft (United States)
Ryen W. White
Ryen W. White Microsoft (United States)
George Hripcsak
George Hripcsak Columbia University
Laszlo Urban
Laszlo Urban Novartis (Switzerland)
Olivier Bodenreider
Olivier Bodenreider National Institutes of Health

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