Congratulations to co-first authors Dr. Savannah Bifluco and Matthew Magoon, as well as Pat and co-senior author Dr. Nazem Akoum, on their new paper “Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning”. Aiming to address clinical-ready technologies, this algorithm blends EHR and LGE-derived data to explain arrhythmia recurrence risk in ablation-treated patients via SHAP analysis. We are particularly proud to publish two major datasets alongside this paper, one containing 164 finite element meshes reconstructed from MRI scans of 82 AF patients (dryad doi: 10.5061/dryad.kkwh70sg0), the other containing all source code necessary to reproduce, remix, and extend our scientific work (dryad doi: 10.5061/dryad.kkwh70sg0). The article is accessible through Nature Communications Medicine and PubMed.
