Deep Learning Approaches for LVAD Candidacy Assessment and Cardiac ICU LVAD-related Outcome Prediction

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dc.contributor.advisorCavallaro, Joseph R
dc.creatorMendoza Gonzales, Antonio
dc.date.accessioned2024-05-22T15:58:32Z
dc.date.available2024-05-22T15:58:32Z
dc.date.created2024-05
dc.date.issued2024-04-18
dc.date.submittedMay 2024
dc.date.updated2024-05-22T15:58:32Z
dc.description.abstractThis thesis explores Deep Learning systems in advanced heart failure and Left Ventricular Assist Device (LVAD) candidacy assessment. We developed deep learning classification and semantic segmentation for 12-lead Electrocardiograms (ECG) from multiple datasets, proposed criteria for candidacy, and implemented interpretability with saliency maps and uncertainty awareness, to aid in LVAD candidate selection. Furthermore, we analyze long-term LVAD patient ECG pre-implantation progressions using an external test set. In a second aspect, we link hemodynamic response to inotropes with VAD-related outcomes by analyzing physiological time series using an ensemble approach for multi-modal waveforms in the pediatric cardiac intensive care setting of a Houston-based hospital. Our system processes minute-by-minute data with the aim of identifying the need for mechanical circulatory support. The predictions help towards early identification of high-risk patients after only two days from admission, and the estimation of feature importance confirms the predictive ability of the hemodynamic early response to inotropes.
dc.format.mimetypeapplication/pdf
dc.identifier.citationGonzales, Antonio Mendoza. Deep Learning Approaches for LVAD Candidacy Assessment and Cardiac ICU LVAD-related Outcome Prediction. (2024). Masters thesis, Rice University. https://hdl.handle.net/1911/116180
dc.identifier.urihttps://hdl.handle.net/1911/116180
dc.language.isoeng
dc.rightsCopyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
dc.subjectdeep learning
dc.subjectheart failure
dc.subjectleft ventricular assist device
dc.subjectLVAD
dc.subjectICU outcome prediction
dc.titleDeep Learning Approaches for LVAD Candidacy Assessment and Cardiac ICU LVAD-related Outcome Prediction
dc.typeThesis
dc.type.materialText
thesis.degree.departmentElectrical and Computer Engineering
thesis.degree.disciplineEngineering
thesis.degree.grantorRice University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
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