Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare

View ORCID ProfileZohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski, Helena Andres Terre, Paul Scherer, Urška Matjašec, Jean Abraham, Pietro Liò, Mateja Jamnik
doi: https://doi.org/10.1101/2021.01.22.427799
Zohreh Shams
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Zohreh Shams
  • For correspondence: zohreh.shams@cst.cam.ac.uk
Botty Dimanov
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Sumaiyah Kola
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nikola Simidjievski
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Helena Andres Terre
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Paul Scherer
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Urška Matjašec
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jean Abraham
2Department of Oncology, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
3Cambridge Breast Cancer Research Unit, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
4NIHR Cambridge Biomedical Research Centre, Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Pietro Liò
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mateja Jamnik
1Department of Computer Science and Technology, University of Cambridge, UK
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Deep learning models are receiving increasing attention in clinical decision-making, however the lack of interpretability and explainability impedes their deployment in day-to-day clinical practice. We propose REM, an interpretable and explainable methodology for extracting rules from deep neural networks and combining them with other data-driven and knowledge-driven rules. This allows integrating machine learning and reasoning for investigating applied and basic biological research questions. We evaluate the utility of REM on the predictive tasks of classifying histological and immunohistochemical breast cancer subtypes from genotype and phenotype data. We demonstrate that REM efficiently extracts accurate, comprehensible and, biologically relevant rulesets from deep neural networks that can be readily integrated with rulesets obtained from tree-based approaches. REM provides explanation facilities for predictions and enables the clinicians to validate and calibrate the extracted rulesets with their domain knowledge. With these functionalities, REM caters for a novel and direct human-in-the-loop approach in clinical decision making.

Competing Interest Statement

The authors have declared no competing interest.

  • Abbreviations

    ML
    Machine Learning
    DNN
    Deep Neural Network
    REM
    Rule Extraction Methodology
    REM-D
    Rule Extraction Methodology from Deep Neural Networks
    REM-T
    Rule Extraction Methodology from Trees
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
    Back to top
    PreviousNext
    Posted January 24, 2021.
    Download PDF
    Email

    Thank you for your interest in spreading the word about bioRxiv.

    NOTE: Your email address is requested solely to identify you as the sender of this article.

    Enter multiple addresses on separate lines or separate them with commas.
    REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
    (Your Name) has forwarded a page to you from bioRxiv
    (Your Name) thought you would like to see this page from the bioRxiv website.
    CAPTCHA
    This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
    Share
    REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
    Zohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski, Helena Andres Terre, Paul Scherer, Urška Matjašec, Jean Abraham, Pietro Liò, Mateja Jamnik
    bioRxiv 2021.01.22.427799; doi: https://doi.org/10.1101/2021.01.22.427799
    Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
    Citation Tools
    REM: An Integrative Rule Extraction Methodology for Explainable Data Analysis in Healthcare
    Zohreh Shams, Botty Dimanov, Sumaiyah Kola, Nikola Simidjievski, Helena Andres Terre, Paul Scherer, Urška Matjašec, Jean Abraham, Pietro Liò, Mateja Jamnik
    bioRxiv 2021.01.22.427799; doi: https://doi.org/10.1101/2021.01.22.427799

    Citation Manager Formats

    • BibTeX
    • Bookends
    • EasyBib
    • EndNote (tagged)
    • EndNote 8 (xml)
    • Medlars
    • Mendeley
    • Papers
    • RefWorks Tagged
    • Ref Manager
    • RIS
    • Zotero
    • Tweet Widget
    • Facebook Like
    • Google Plus One

    Subject Area

    • Bioinformatics
    Subject Areas
    All Articles
    • Animal Behavior and Cognition (2528)
    • Biochemistry (4972)
    • Bioengineering (3481)
    • Bioinformatics (15204)
    • Biophysics (6895)
    • Cancer Biology (5389)
    • Cell Biology (7738)
    • Clinical Trials (138)
    • Developmental Biology (4530)
    • Ecology (7146)
    • Epidemiology (2059)
    • Evolutionary Biology (10225)
    • Genetics (7511)
    • Genomics (9785)
    • Immunology (4842)
    • Microbiology (13213)
    • Molecular Biology (5138)
    • Neuroscience (29421)
    • Paleontology (203)
    • Pathology (836)
    • Pharmacology and Toxicology (1463)
    • Physiology (2137)
    • Plant Biology (4747)
    • Scientific Communication and Education (1010)
    • Synthetic Biology (1338)
    • Systems Biology (4012)
    • Zoology (768)