Interpretable Multimodal Learning for Cardiovascular Hemodynamics Assessment
This work addresses the need for non-invasive screening of high-risk heart failure patients from large populations, representing an incremental improvement with domain-specific impact.
The paper tackles the problem of non-invasive prediction of Pulmonary Arterial Wedge Pressure (PAWP) for heart failure detection by proposing an interpretable multimodal learning pipeline that combines Cardiac Magnetic Resonance Imaging (CMR) scans and Electronic Health Records (EHRs), achieving superior performance compared to state-of-the-art methods on a dataset of 2,641 subjects.
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.