[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126549-en":3,"doc-seo-126549-105":31,"detail-sidebar-cat-0-en-105":92},{"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126549,13056712833777,"Logic","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",6,"Technology","KoopaML - A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals","Machine Learning (ML) supports complex data analyses across domains, and the medical field—where large volumes of data are generated continuously—can benefit from ML pipelines for tasks such as diagnosis, classification, disease detection, segmentation, and organ-function assessment. Despite their domain expertise, many health professionals may lack programming and theoretical ML knowledge, limiting effective use of ML algorithms on clinical data. KoopaML is presented as a platform that helps non-expert users define ML pipelines in healthcare, emphasizing education on model behavior, outcome interpretation, and a flexible architecture for evolving components and heuristics.","KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals  \nFrancisco José García-Peñalvo1, Andrea Vázquez-Ingelmo1, Alicia García-Holgado1, Jesús SampedroGómez2, Antonio Sánchez-Puente2, Víctor Vicente-Palacios3, P. Ignacio Dorado-Díaz2, Pedro L. Sánchez2 *  \n1 GRIAL Research Group, University of Salamanca (Spain)  \n2 Cardiology Department, Hospital Universitario de Salamanca, SACyL. IBSAL, Facultad de Medicina, University of Salamanca, and CIBERCV (ISCiii) (Spain)  \n3 Philips Healthcare (Spain)  \nReceived 23 February 2022 | Accepted 1 June 2022 | Early Access 23 January 2023  \nAbstract   \nMachine Learning (ML) has extended its use in several domains to support complex analyses of data. The medical field, in which significant quantities of data are continuously generated, is one of the domains that can benefit from the application of ML pipelines to solve specific problems such as diagnosis, classification, disease detection, segmentation, assessment of organ functions, etc. However, while health professionals are experts in their domain, they can lack programming and theoretical skills regarding ML applications. Therefore, it is necessary to train health professionals in using these paradigms to get the most out of the application of ML algorithms to their data. In this work, we present a platform to assist non-expert users in defining ML pipelinesin the health domain. The system’s design focuses on providing an educational experience to understand how ML algorithms work and how to interpret their outcomes and on fostering a flexible architecture to allow the evolution of the available components, algorithms, and heuristics.  \nI. Introduction  \nMACHINE Learning (ML) has become a powerful approach to  \ntackle complex tasks that involve analyzing significant amounts of data. Data-intensive contexts, such as the health domain, benefit directly from applying ML algorithms to their data, supporting tasks such as identifying patterns, clustering, classification, predictions, etc., that could become time-and resource-consuming if approached through manual paradigms. The application of ML to health data has proven its usefulness in specific challenges like diagnoses, disease detection, segmentation, assessment of organ functions, etc. [1] -[3] . However, applying ML approaches is not straightforward. More specifically, using them in sensitive domains (such as health) could be hazardous if practitioners do not fully understand the results derived from the models.  \nML does not only consist of applying a set of pre-defined functions. It needs a deep understanding of the input data, the transformations that need to be performed to fit a model, the selection of a proper  \n* Corresponding author.  \nE-mail addresses: [fgarcia@usal.es](fgarcia@usal.es) (F. J. García-Peñalvo), [andreavazquez@usal.es](andreavazquez@usal.es) (A. Vázquez-Ingelmo), [aliciagh@usal.es](aliciagh@usal.es) (A. García-Holgado), [jmsampedro@saludcastillayleon.es](jmsampedro@saludcastillayleon.es) (J. SampedroGómez), [asanchezpu@saludcastillayleon.es](asanchezpu@saludcastillayleon.es) (A. Sánchez-Puente), [victor.vicente.palacios@philips.com](victor.vicente.palacios@philips.com) (V. Vicente-Palacios), pidorado@ [saludcastillayleon.es](saludcastillayleon.es) (P. Ignacio Dorado-Díaz), plsanchez@  \n[saludcastillayleon.es](saludcastillayleon.es) (P. L. Sánchez).  \nmodel, and its quality metrics before using trained models in production. Otherwise, the outputs could lead to wrong conclusions, losses, discrimination, and even negligence [4] -[7] .  \nTherefore, it is necessary to balance data domain knowledge and ML expertise. While ML experts have a wealth of knowledge about ML algorithms, they can lack understanding regarding the input data. The same applies to health professionals; they have a profound knowledge of the data domain, but they would not obtain quality models without programming or ML skills.  \nIn this scenario, it i","cbCaipWTWuYGnUeX","https://ap.wps.com/l/cbCaipWTWuYGnUeX","pdf",1776887,3,1,8,"English","en",105,"# Introduction\n## Research question and target audience\n## Platform goals and user-centered design","[{\"question\":\"Why do health professionals need support when applying machine learning in medical contexts?\",\"answer\":\"Because applying ML in sensitive healthcare settings is not straightforward and can lead to harmful outcomes if practitioners do not fully understand model results. Health professionals may also lack programming and ML theoretical skills, creating a knowledge gap.\"},{\"question\":\"What is KoopaML designed to help users do?\",\"answer\":\"KoopaML provides an intuitive, educational graphical interface that enables non-expert users to define and run ML pipelines for healthcare data. It also supports learning how algorithms work and how to interpret their outcomes.\"},{\"question\":\"How does the platform support future extension by expert users?\",\"answer\":\"KoopaML follows a flexible architecture that lets expert users extend functionality with new custom algorithms, components, or heuristics to guide ML pipeline definition.\"}]","KoopaML - A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals | PDF",1785933261,20,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"koopaml-a-graphical-platform-for-building-machine-learning-pipelines-adapted-to-health-professionals","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/technology/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/koopaml-a-graphical-platform-for-building-machine-learning-pipelines-adapted-to-health-professionals/126549/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why do health professionals need support when applying machine learning in medical contexts?","Question",{"text":76,"@type":77},"Because applying ML in sensitive healthcare settings is not straightforward and can lead to harmful outcomes if practitioners do not fully understand model results. Health professionals may also lack programming and ML theoretical skills, creating a knowledge gap.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is KoopaML designed to help users do?",{"text":81,"@type":77},"KoopaML provides an intuitive, educational graphical interface that enables non-expert users to define and run ML pipelines for healthcare data. It also supports learning how algorithms work and how to interpret their outcomes.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the platform support future extension by expert users?",{"text":85,"@type":77},"KoopaML follows a flexible architecture that lets expert users extend functionality with new custom algorithms, components, or heuristics to guide ML pipeline definition.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,114,119,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":112,"slug":113},50,"technology",{"id":115,"doc_module":4,"doc_module_name":47,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":120,"show_sort_weight":121,"slug":122},"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":30,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":30,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":30,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]