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Explainable Graph N...
Explainable Graph Neural Networks for Atherosclerotic Cardiovascular Disease
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- Lundström, Jens, 1981- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Hashemi, Atiye Sadat, 1991- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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- Tiwari, Prayag, 1991- (författare)
- Högskolan i Halmstad,Akademin för informationsteknologi
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(creator_code:org_t)
- Amsterdam : IOS Press, 2023
- 2023
- Engelska.
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Ingår i: Caring is sharing - exploiting the value in data for health and innovation. - Amsterdam : IOS Press. - 9781643683881 ; , s. 603-604
- Relaterad länk:
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https://doi.org/10.3...
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visa fler...
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https://urn.kb.se/re...
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https://doi.org/10.3...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Medicinska och farmaceutiska grundvetenskaper -- Neurovetenskaper (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Basic Medicine -- Neurosciences (hsv//eng)
Nyckelord
- Cardiovascular Diseases
- EHR
- Graph Neural Networks
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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