Tripathi, P.C., Tabakhi, S., Suvon, M.N.I. orcid.org/0000-0001-9962-315X et al. (5 more authors) (Submitted: 2024) Interpretable multimodal learning for cardiovascular hemodynamics assessment. [Preprint - arXiv] (Submitted)
Abstract
Pulmonary Arterial Wedge Pressure (PAWP) is an essential cardiovascular hemodynamics marker to detect heart failure. In clinical practice, Right Heart Catheterization is considered a gold standard for assessing cardiac hemodynamics while non-invasive methods are often needed to screen high-risk patients from a large population. In this paper, we propose a multimodal learning pipeline to predict PAWP marker. We utilize complementary information from Cardiac Magnetic Resonance Imaging (CMR) scans (short-axis and four-chamber) and Electronic Health Records (EHRs). We extract spatio-temporal features from CMR scans using tensor-based learning. We propose a graph attention network to select important EHR features for prediction, where we model subjects as graph nodes and feature relationships as graph edges using the attention mechanism. We design four feature fusion strategies: early, intermediate, late, and hybrid fusion. With a linear classifier and linear fusion strategies, our pipeline is interpretable. We validate our pipeline on a large dataset of 2,641 subjects from our ASPIRE registry. The comparative study against state-of-the-art methods confirms the superiority of our pipeline. The decision curve analysis further validates that our pipeline can be applied to screen a large population. The code is available at: https://github.com/prasunc/hemodynamics
Metadata
Item Type: | Preprint |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Author(s). This preprint is made available under a Creative Commons Attribution 4.0 International License. (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Cardiac Hemodynamics; Feature Selection; Interpretable Model; Multimodal Learning; Pulmonary Arterial Wedge Pressure; Transformer |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number WELLCOME TRUST (THE) 215799/Z/19/Z |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 18 Oct 2024 14:48 |
Last Modified: | 18 Oct 2024 14:48 |
Status: | Submitted |
Identification Number: | 10.48550/arXiv.2404.04718 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:218501 |