[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117088-en":3,"doc-seo-117088-105":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":4,"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},117088,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","A Review on Machine Learning Applications - CVI Risk Assessment","This subject review summarizes peer-reviewed evidence on machine learning methods applied to cardiovascular intervention (CVI) risk assessment in cardiac surgery. It focuses on short-term outcome prediction models that use clinical data for decision-making and operational risk estimation. Thirteen articles meeting predefined criteria are analyzed and organized into tables, highlighting study characteristics, method choices, and performance targets. The review shows the value of ML in high-risk CVI settings, clarifies gaps for further improvement, and supports future healthcare prediction model development.","ISSN 1330-3651 (Print), ISSN 1848-6339 (Online) [https://doi.org/10.17559/TV-20230326000480](https://doi.org/10.17559/TV-20230326000480)  \nSubject review  \nA Review on Machine Learning Applications: CVI Risk Assessment  \nAyşe Banu BİRL İK*, Hakan TOZAN, Kevser Banu KÖSE  \nAbstract: Comprehensive literature has been published on the development of digital health applications using machine learning methods in cardiovascular surgery. Many machine learning methods have been applied in clinical decision-making processes, particularly for risk estimation models. This review of the literature shares an update on machine learning applications for cardiovascular intervention (CVI) risk assessment. This study selected peer-reviewed scientific publications providing sufficient detail about machine learning methods and outcomes predicting short-term CVI risk in cardiac surgery. Thirteen articles fulfilling pre-set criteria were reviewed and tables were created presenting the relevant characteristics of the studies. The review demonstrates the usefulness of machine learning methods in high-risk CVI applications, identifies the need for improvement, and provides efficient support for future prediction models for the healthcare system.  \nKeywords: cardiovascular; decision-making; machine learning; prediction model; risk assessment  \n1 INTRODUCTION  \nIn our technology-driven world, developments in digital transformation practices in the health field have rapidly accelerated. Artificial Intelligence (AI) is integral to this. AI refers to information technologies or models that can mimic human intelligence. Machine learning (ML) is a sophisticated subset of AI that employs a data-driven approach to extract profound insights from cumulative data, learning data behavior through an algorithmic framework. The presence of big data has accelerated data mining and expanded ML development into decisionmaking processes in research areas that include engineering, finance, management, and medicine [1-4] . AI and ML applications in health science can be applied in such sub-activity subjects as medical diagnosis and followup, cost estimation, resource planning, and emergency strategy management.  \nHealth systems have expanding literature on machine learning-based algorithms and their potential clinical benefits in the diagnosis and treatment of diseases. Results analysis using clinical data efficiently is of great importance. ML methods for advancing fields such as outcome prediction, diagnosis, medical image interpretation, and treatment in medical science, utilizing large databases accumulated over time, are being developed [5-8] . Although ML use has begun in different segments of health systems to estimate by displaying significant information and estimations from data accumulation, few studies yet exist on its use in high-risk surgical cardiovascular interventions (CVI) [9] .  \nCardiovascular disease (CVD) affects the heart or blood vessels. Coronary artery disease (CAD), such as angina and myocardial infarction, is a type of CVD usually caused by atherosclerosis, an accumulation of plaque inside the artery walls. If the accumulation causes a blockage that narrows these vessels, a decrease in the blood flow which supplies the heart could result in heart attack or paralysis [10] . CAD is one of the most common causes of death in the world, and has been reported to significantly increase overall health care costs [11] .  \nCoronary artery bypass grafting (CABG) is a surgical procedure to treat coronary artery disease that redirects blood around a section of a blocked or partially blocked artery in the heart. CABG is a high-cost, high-risk surgery  \nwith a death rate of approximately 3-5%[12] and high morbidity and loss of life (LoL) risks associated with intraoperative and postoperative complications. Still, CABG is the \"gold standard\" treatment for multi-core coronary artery disease, especially for three-vein or left main coronary artery disease, and rem","cbCair3HiUhLIrrF","https://ap.wps.com/l/cbCair3HiUhLIrrF","pdf",525727,1,9,"English","en",105,"# Introduction\n## Cardiovascular disease background and CABG clinical context\n## Operative risk prediction and limitations of existing scores\n## Motivation for ML-based CVI risk assessment","[{\"question\":\"What does the review focus on in cardiovascular intervention (CVI) risk assessment?\",\"answer\":\"The review updates machine learning applications used to predict short-term CVI risk in cardiac surgery and summarizes evidence on relevant methods and outcomes.\"},{\"question\":\"Why are traditional risk scores a limitation for clinical decision-making?\",\"answer\":\"Risk scoring systems such as EuroSCORE may overestimate real risk, which can negatively influence intervention decisions and provide unrealistic information to patients and families.\"},{\"question\":\"How were studies selected for the review?\",\"answer\":\"The review included peer-reviewed scientific publications that provided sufficient details about machine learning methods and short-term CVI risk prediction outcomes; thirteen articles met the predefined criteria.\"}]","A Review on Machine Learning Applications - CVI Risk Assessment | PDF",1785673692,23,{"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},"a-review-on-machine-learning-applications-cvi-risk-assessment","",{"@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/a-review-on-machine-learning-applications-cvi-risk-assessment/117088/",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","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the review focus on in cardiovascular intervention (CVI) risk assessment?","Question",{"text":75,"@type":76},"The review updates machine learning applications used to predict short-term CVI risk in cardiac surgery and summarizes evidence on relevant methods and outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are traditional risk scores a limitation for clinical decision-making?",{"text":80,"@type":76},"Risk scoring systems such as EuroSCORE may overestimate real risk, which can negatively influence intervention decisions and provide unrealistic information to patients and families.",{"name":82,"@type":73,"acceptedAnswer":83},"How were studies selected for the review?",{"text":84,"@type":76},"The review included peer-reviewed scientific publications that provided sufficient details about machine learning methods and short-term CVI risk prediction outcomes; thirteen articles met the predefined criteria.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]