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Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease

Lundström, Jens ; Hashemi, Atiye Sadat LU and Tiwari, Prayag (2023) In Studies in Health Technology and Informatics 302. p.603-604
Abstract
Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability... (More)
Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability of the low-density lipoprotein cholesterol level in long-term atherosclerotic cardiovascular disease progression and treatment. (Less)
Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
host publication
Caring is Sharing – Exploiting the Value in Data for Health and Innovation : Proceedings of MIE 2023 - Proceedings of MIE 2023
series title
Studies in Health Technology and Informatics
volume
302
pages
603 - 604
publisher
IOS Press
external identifiers
  • scopus:85159762049
DOI
10.3233/SHTI230214
language
English
LU publication?
no
id
78188a0a-9310-4c3d-aa8f-3c2a87d49143
date added to LUP
2025-01-31 14:28:35
date last changed
2025-03-13 04:01:20
@inproceedings{78188a0a-9310-4c3d-aa8f-3c2a87d49143,
  abstract     = {{Understanding the aspects of progression for atherosclerotic cardiovascular disease and treatment is key to building reliable clinical decision-support systems. To promote system trust, one step is to make the machine learning models (used by the decision support systems) explainable for clinicians, developers, and researchers. Recently, working with longitudinal clinical trajectories using Graph Neural Networks (GNNs) has attracted attention among machine learning researchers. Although GNNs are seen as black-box methods, promising explainable AI (XAI) methods for GNNs have lately been proposed. In this paper, which describes initial project stages, we aim at utilizing GNNs for modeling, predicting, and exploring the model explainability of the low-density lipoprotein cholesterol level in long-term atherosclerotic cardiovascular disease progression and treatment.}},
  author       = {{Lundström, Jens and Hashemi, Atiye Sadat and Tiwari, Prayag}},
  booktitle    = {{Caring is Sharing – Exploiting the Value in Data for Health and Innovation : Proceedings of MIE 2023}},
  language     = {{eng}},
  pages        = {{603--604}},
  publisher    = {{IOS Press}},
  series       = {{Studies in Health Technology and Informatics}},
  title        = {{Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease}},
  url          = {{http://dx.doi.org/10.3233/SHTI230214}},
  doi          = {{10.3233/SHTI230214}},
  volume       = {{302}},
  year         = {{2023}},
}