tutorial

Interpretable Machine Learning in Healthcare

Authors:

Muhammad Aurangzeb Ahmad,

Ankur TeredesaiAuthors Info & Claims

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Pages 559 - 560

https://doi.org/10.1145/3233547.3233667

Published: 15 August 2018 Publication History

Abstract

This tutorial extensively covers the definitions, nuances, challenges, and requirements for the design of interpretable and explainable machine learning models and systems in healthcare. We discuss many uses in which interpretable machine learning models are needed in healthcare and how they should be deployed. Additionally, we explore the landscape of recent advances to address the challenges model interpretability in healthcare and also describe how one would go about choosing the right interpretable machine learnig algorithm for a given problem in healthcare.

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Bibi NCourtney JMcGuinness K(2025)Enhancing Brain Disease Diagnosis with XAI: A Review of Recent StudiesACM Transactions on Computing for Healthcare10.1145/3709152Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3709152
Chen HDreizin DGomez CZapaishchykova AUnberath M(2025)Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian InferenceIEEE Transactions on Medical Imaging10.1109/TMI.2024.342883644:1(130-141)Online publication date: Jan-2025
https://doi.org/10.1109/TMI.2024.3428836
He DWang RXu ZWang JSong PWang HSu J(2024)The use of artificial intelligence in the treatment of rare diseases: A scoping reviewIntractable & Rare Diseases Research10.5582/irdr.2023.0111113:1(12-22)Online publication date: 29-Feb-2024
https://doi.org/10.5582/irdr.2023.01111
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Index Terms

Interpretable Machine Learning in Healthcare
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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Information

Published In

cover image ACM Conferences

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2018

727 pages

ISBN:9781450357944

DOI:10.1145/3233547

General Chairs:
Amarda Shehu
George Mason University, USA
,
Cathy Wu
University of Delaware, USA
,
Program Chairs:
Christina Boucher
University of Florida, USA
,
Jing Li
Case Western Reserve University, USA
,
Hongfang Liu
Mayo Clinic, USA
,
Mihai Pop
University of Maryland, USA

Copyright © 2018 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGBio: ACM Special Interest Group on Bioinformatics

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 August 2018

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BCB '18

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SIGBio

BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 29 - September 1, 2018

DC, Washington, USA

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BCB '18 Paper Acceptance Rate 46 of 148 submissions, 31%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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Cited By

Bibi NCourtney JMcGuinness K(2025)Enhancing Brain Disease Diagnosis with XAI: A Review of Recent StudiesACM Transactions on Computing for Healthcare10.1145/3709152Online publication date: 9-Jan-2025
https://dl.acm.org/doi/10.1145/3709152
Chen HDreizin DGomez CZapaishchykova AUnberath M(2025)Interpretable Severity Scoring of Pelvic Trauma Through Automated Fracture Detection and Bayesian InferenceIEEE Transactions on Medical Imaging10.1109/TMI.2024.342883644:1(130-141)Online publication date: Jan-2025
https://doi.org/10.1109/TMI.2024.3428836
He DWang RXu ZWang JSong PWang HSu J(2024)The use of artificial intelligence in the treatment of rare diseases: A scoping reviewIntractable & Rare Diseases Research10.5582/irdr.2023.0111113:1(12-22)Online publication date: 29-Feb-2024
https://doi.org/10.5582/irdr.2023.01111
Kang MLin ZSun JXiao CLi BSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Certifiably byzantine-robust federated conformal predictionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692996(23022-23057)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3692996
Tiwari N(2024)Transformative AI in Biomedicine AnalysisApplication of Large Language Models (LLMs) for Software Vulnerability Detection10.4018/979-8-3693-9311-6.ch006(167-262)Online publication date: 18-Oct-2024
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Kumar NTripathi PNanda RTiwari SSharma S(2024)Machine Learning for Smart Health Services in the Framework of Industry 5.0Infrastructure Possibilities and Human-Centered Approaches With Industry 5.010.4018/979-8-3693-0782-3.ch013(215-230)Online publication date: 19-Jan-2024
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Msheik BAdda MMcheick HDbouk M(2024)Survey on Knowledge Representation Models in HealthcareInformation10.3390/info1508043515:8(435)Online publication date: 26-Jul-2024
https://doi.org/10.3390/info15080435
Maeda-Gutiérrez VGalván-Tejada CGalván-Tejada JCruz MCelaya-Padilla JGamboa-Rosales HGarcía-Hernández ALuna-García HVillalba-Condori K(2024)Evaluating Feature Selection Methods for Accurate Diagnosis of Diabetic Kidney DiseaseBiomedicines10.3390/biomedicines1212285812:12(2858)Online publication date: 16-Dec-2024
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AbdelAziz NSaid WAbdelHafeez MAli A(2024)Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AIFrontiers in Artificial Intelligence10.3389/frai.2024.14560697Online publication date: 2-Sep-2024
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Zhao MZhou HWang JLiu YZhang X(2024)A new method for identification of traditional Chinese medicine constitution based on tongue features with machine learningTechnology and Health Care10.3233/THC-24012832:5(3393-3408)Online publication date: 3-Sep-2024
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