Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease
(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:
https://lup.lub.lu.se/record/78188a0a-9310-4c3d-aa8f-3c2a87d49143
- author
- Lundström, Jens ; Hashemi, Atiye Sadat LU and Tiwari, Prayag
- publishing date
- 2023
- 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}}, }