World's Best Scientists 2026 revealed!

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Computer Science

D-Index
61
Citations
26172
World Ranking
2996
National Ranking
180

Overview

Norman Fenton is affiliated with Queen Mary University of London in the United Kingdom. Their research spans multiple areas within computer science and medicine, focusing especially on artificial intelligence and health information management. The scientist has contributed extensively to the understanding and application of Bayesian modeling and causal inference in healthcare contexts.

The main fields of study for Norman Fenton include:

  • Computer Science
  • Medicine

Their work covers several subfields, notably:

  • Artificial Intelligence
  • Health Information Management
  • Modeling and Simulation
  • Management Science and Operations Research
  • Information Systems

Regarding research topics, Norman Fenton has focused on:

  • Bayesian Modeling and Causal Inference
  • Machine Learning in Healthcare
  • Electronic Health Records Systems
  • COVID-19 Epidemiological Studies
  • Decision-Making and Behavioral Economics
  • Artificial Intelligence in Healthcare
  • Risk and Safety Analysis

Selected recent papers authored or co-authored by Norman Fenton include:

  • COVID-19 infection and death rates: the need to incorporate causal explanations for the data and avoid bias in testing, 2020, Journal of Risk Research
  • Learning from Behavioural Changes That Fail, 2020, Trends in Cognitive Sciences
  • Bayesian networks in healthcare: What is preventing their adoption?, 2021, Artificial Intelligence in Medicine
  • Medical idioms for clinical Bayesian network development, 2020, Journal of Biomedical Informatics
  • mHealth apps for gestational diabetes mellitus that provide clinical decision support or artificial intelligence: A scoping review, 2021, Diabetic Medicine

Frequent co-authors collaborating with Norman Fenton include:

  • Martin Neil
  • Scott McLachlan
  • Evangelia Kyrimi
  • Kudakwashe Dube
  • Magda Osman

Norman Fenton has published in various venues, with multiple contributions to:

  • arXiv (Cornell University)
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Artificial Intelligence in Medicine
  • Lara D. Veeken
  • Journal of Risk Research

In addition to journal publications, Norman Fenton has authored a book titled Shaken Baby Syndrome, published by Cambridge University Press in 2023.

Best Publications

  • Software Metrics: A Rigorous and Practical Approach

    Norman E. Fenton;Shari Lawrence Pfleeger

  • A critique of software defect prediction models

    N.E. Fenton;M. Neil

  • Software Metrics: A Rigorous Approach

    Norman E. Fenton

  • Software metrics (2nd ed.): a rigorous and practical approach

    Norman Fenton;Shari Lawrence Pfleeger

  • Risk Assessment and Decision Analysis with Bayesian Networks

    Norman Fenton;Martin Neil

  • Quantitative analysis of faults and failures in a complex software system

    N.E. Fenton;N. Ohlsson

  • Software measurement: a necessary scientific basis

    N. Fenton

  • Towards a framework for software measurement validation

    B. Kitchenham;S.L. Pfleeger;N. Fenton

  • Software metrics: roadmap

    Norman E. Fenton;Martin Neil

  • Science and substance: a challenge to software engineers

    N. Fenton;S.L. Pfleeger;R.L. Glass

  • Building large-scale Bayesian networks

    Martin Neil;Norman Fenton;Lars Nielson

  • Software metrics: success, failures and new directions

    Norman E. Fenton;Martin Neil

  • Towards Operational Measures of Computer Security

    Bev Littlewood;Sarah Brocklehurst;Norman Fenton;Peter Mellor

  • Software Metrics: A Rigorous and Practical Approach, Third Edition

    Norman Fenton;James Bieman

  • Using Ranked Nodes to Model Qualitative Judgments in Bayesian Networks

    Norman E. Fenton;Martin Neil;Jose Galan Caballero

  • Predicting software defects in varying development lifecycles using Bayesian nets

    Norman Fenton;Martin Neil;William Marsh;Peter Hearty

  • Software measurement: uncertainty and causal modeling

    N. Fenton;P. Krause;M. Neil

  • A General Structure for Legal Arguments about Evidence Using Bayesian Networks.

    Norman E. Fenton;Martin Neil;David A. Lagnado

  • Using Bayesian networks to model expected and unexpected operational losses.

    Martin Neil;Norman Fenton;Manesh Tailor

  • Software Metrics

    N.E. Fenton

Frequent Co-Authors

Martin Neil
Martin Neil Queen Mary University of London
David A. Lagnado
David A. Lagnado University College London
Bev Littlewood
Bev Littlewood City, University of London
James M. Bieman
James M. Bieman Colorado State University
Shari Lawrence Pfleeger
Shari Lawrence Pfleeger Dartmouth College
Barbara Kitchenham
Barbara Kitchenham Keele University
Keith D. Shepherd
Keith D. Shepherd World Agroforestry Centre
Eike Luedeling
Eike Luedeling University of Bonn
Henry W. W. Potts
Henry W. W. Potts University College London
Tracy Hall
Tracy Hall Lancaster University

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