[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122385-en":3,"doc-seo-122385-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122385,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Multimodal Machine Learning Framework for Outcome Prediction in Congenital Heart Disease - PhD Thesis","Congenital Heart Disease (CHD) affects approximately 1.2 million newborns annually worldwide, with about 4,600 cases reported in the UK each year. CHD includes diverse structural defects that complicate early diagnosis, risk stratification, and treatment planning. Traditional predictive methods are limited by anatomical and functional heterogeneity, small datasets, and reliance on single-modal clinical markers, reducing generalisability. This PhD thesis investigates a multimodal machine learning framework using geometric learning and multi-modal integration to improve CHD classification and outcome prediction under heterogeneous clinical data conditions.","Alkan, Muhammet (2025) Multimodal machine learning framework for outcome prediction in congenital heart disease. PhD thesis.  \n[https://theses.gla.ac.uk/85428/](https://theses.gla.ac.uk/85428/)  \nCopyright and moral rights for this work are retained by the author A copy can be downloaded for personal non-commercial research or study, without prior permission or charge  \nThis work cannot be reproduced or quoted extensively from without first obtaining permission from the author  \nThe content must not be changed in any way or sold commercially in any format or medium without the formal permission of the author  \nWhen referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given  \nEnlighten: Theses  \n[https://theses.gla.ac.uk/](https://theses.gla.ac.uk/)  \n[research-enlighten@glasgow.ac.uk](research-enlighten@glasgow.ac.uk)  \nMultimodal Machine Learning Framework for Outcome Prediction in Congenital Heart Disease  \nMuhammet Alkan  \nAdvisor: Dr Fani Deligianni  \nSubmitted in fulfilment of the requirements for the Degree of Doctor of Philosophy  \nSchool of Engineering  \nCollege of Science and Engineering  \nUniversity of Glasgow  \nApril 2025  \nAbstract  \nCongenital Heart Disease (CHD) affects approximately 1.2 million newborns annually worldwide, with around 4,600 cases occurring in the UK each year. CHD encompasses a complex set of structural heart defects that pose challenges in early diagnosis, risk stratification, and treatment planning. Traditional methods employed for predicting clinical outcomes constrained by the pronounced anatomical and functional heterogeneity, limited number of datasets, and single-modal clinical markers, which often hinders the development of generalisable models in congenital heart diseases. Recent advancements in the field of Machine Learning (ML) and Deep Learning (DL) offer opportunities to integrate multi-modal data sources, thereby enabling a more comprehensive understanding of patient health. This thesis explores a multi-modal machine learning framework designed to improve CHD classification and outcome prediction, by integrating multi-modal data and geometric learning.  \nA significant challenge encountered during the course of this research is the heterogeneity characteristic of clinical data sources. Patient records contain Electrocardiogram (ECG) signals, cardiopulmonary exercise testing metrics and unstructured clinical documentation, each with different formats and level of completeness. Furthermore, the inherent anatomical and physiological heterogeneity of CHD increases the complexity of predictive performance. It is important to note that a model trained on one subtype may exhibit suboptimal performance when applied to a different CHD presentation, making generalisation across diverse patient populations a challenge. This thesis attempts to bridge these gaps by leveraging Riemannian geometry for the purpose of feature extraction, employing covariance augmentations to generate more data, and utilising multi-modal data integration to maximise predictive potential.  \nRisk prediction models are statistical or machine learning-based frameworks designed to es-  \ntimate the likelihood of future adverse events for a given patient or population. In the domain of cardiology, these models facilitate predictions about a variety of outcomes, including the risk of mortality and the progression of the disease. This, in turn, serves to inform the development of early intervention and treatment strategies. They often rely on features extracted from clinical data, including ECGs, laboratory results, imaging data, and patient demographics to generate meaningful insights. However, developing accurate risk prediction models with small sample sizes presents several challenges such as limited generalisation, high variance, reduced reliability, and an insufficient representation of rare cases, particularly due to the low prevalence of re","cbCaijaDQlZwGytq","https://ap.wps.com/l/cbCaijaDQlZwGytq","pdf",20617179,1,184,"English","en",105,"# Abstract\n## Problem: Heterogeneity in CHD Data\n## Multimodal Framework and Geometric Learning\n## Risk Prediction and CPET as Surrogate\n## Geometric Deep Learning for ECG Covariance\n## Clinical Focus and Motivation","[{\"question\":\"Why are outcome prediction models challenging in congenital heart disease (CHD)?\",\"answer\":\"CHD involves pronounced anatomical and physiological heterogeneity, limited dataset sizes, and often single-modal clinical markers. A model trained on one CHD subtype may underperform on another, harming generalisation.\"},{\"question\":\"What core approach does the thesis propose to address these challenges?\",\"answer\":\"It explores a multimodal machine learning framework that integrates multi-modal data and uses geometric learning for feature extraction. The work leverages Riemannian geometry and covariance augmentations to improve robustness and predictive potential.\"},{\"question\":\"How does the thesis address small sample size and event imbalance in risk modelling?\",\"answer\":\"It discusses difficulties in developing reliable risk prediction models with small samples and imbalanced datasets. It proposes using Cardiopulmonary Exercise Testing (CPET) as a surrogate for mortality to improve accuracy and reliability, even with limited data.\"}]","Multimodal Machine Learning Framework for Outcome Prediction in Congenital Heart Disease - PhD Thesis | PDF",1785810360,464,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"multimodal-machine-learning-framework-for-outcome-prediction-in-congenital-heart-disease-phd-thesis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multimodal-machine-learning-framework-for-outcome-prediction-in-congenital-heart-disease-phd-thesis/122385/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are outcome prediction models challenging in congenital heart disease (CHD)?","Question",{"text":75,"@type":76},"CHD involves pronounced anatomical and physiological heterogeneity, limited dataset sizes, and often single-modal clinical markers. A model trained on one CHD subtype may underperform on another, harming generalisation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What core approach does the thesis propose to address these challenges?",{"text":80,"@type":76},"It explores a multimodal machine learning framework that integrates multi-modal data and uses geometric learning for feature extraction. The work leverages Riemannian geometry and covariance augmentations to improve robustness and predictive potential.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the thesis address small sample size and event imbalance in risk modelling?",{"text":84,"@type":76},"It discusses difficulties in developing reliable risk prediction models with small samples and imbalanced datasets. It proposes using Cardiopulmonary Exercise Testing (CPET) as a surrogate for mortality to improve accuracy and reliability, even with limited data.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]