[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127240-en":3,"doc-seo-127240-105":29,"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":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},127240,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals - Research paper","Machine Learning (ML) supports complex data analysis, and healthcare is a data-intensive domain where ML pipelines can improve diagnosis, classification, disease detection, segmentation, and organ-function assessment. The main barrier is the skills gap: health professionals may be experts in clinical domains but often lack programming and ML theory, while ML specialists may lack domain understanding. This work introduces KoopaML, a graphical platform enabling non-expert users to define ML pipelines through an educational, interpretable experience and a flexible architecture that supports evolving components, algorithms, and heuristics.","International Journal of Interactive Multimedia and Artificial Intelligence, Vol. 8, Nº6  \nKoopaML: 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)  \n* Corresponding author. [fgarcia@usal.es](fgarcia@usal.es) (F. J. García-Peñalvo), [andreavazquez@usal.es](andreavazquez@usal.es) (A. Vázquez-Ingelmo), aliciagh@ [usal.es](usal.es) (A. García-Holgado), [jmsampedro@saludcastillayleon.es](jmsampedro@saludcastillayleon.es) (J. Sampedro-Gómez), [asanchezpu@saludcastillayleon.es](asanchezpu@saludcastillayleon.es)  \n(A. Sánchez-Puente), [victor.vicente.palacios@philips.com](victor.vicente.palacios@philips.com) (V. Vicente-Palacios), [pidorado@saludcastillayleon.es](pidorado@saludcastillayleon.es) (P. Ignacio  \nDorado-Díaz), [plsanchez@saludcastillayleon.es](plsanchez@saludcastillayleon.es) (P. L. Sánchez).  \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 model, and its quality metrics before using trained models in  \nproduction. 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 woul","cbCaistFFEe8eSfP","https://ap.wps.com/l/cbCaistFFEe8eSfP","pdf",2211962,1,"English","en",105,"# Abstract\n# Introduction\n## Problem and skills gap in healthcare ML\n## Need for user-centered educational and flexible tooling\n# Research question and proposed solution","[{\"question\":\"What problem KoopaML addresses in applying machine learning to healthcare?\",\"answer\":\"KoopaML addresses the difficulty for health professionals to apply ML effectively due to limited programming and ML theoretical skills, while still needing to account for clinical data understanding. It also aims to reduce risk by helping users interpret model outcomes properly.\"},{\"question\":\"Who is the primary target audience of the KoopaML platform?\",\"answer\":\"The platform targets non-expert users who want to learn and apply ML models to domain data in healthcare. It also supports expert users through extensible architecture.\"},{\"question\":\"How does KoopaML help users build and run ML pipelines?\",\"answer\":\"KoopaML provides intuitive and educational graphical interfaces to define and execute ML pipelines. Its design focuses on understanding how algorithms work, interpreting outcomes, and enabling extension with new components, algorithms, or heuristics.\"}]","KoopaML: A Graphical Platform for Building Machine Learning Pipelines Adapted to Health Professionals - Research paper | PDF",1785937693,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"koopaml-a-graphical-platform-for-building-machine-learning-pipelines-adapted-to-health-professionals-research-paper","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/koopaml-a-graphical-platform-for-building-machine-learning-pipelines-adapted-to-health-professionals-research-paper/127240/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",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},"What problem KoopaML addresses in applying machine learning to healthcare?","Question",{"text":75,"@type":76},"KoopaML addresses the difficulty for health professionals to apply ML effectively due to limited programming and ML theoretical skills, while still needing to account for clinical data understanding. It also aims to reduce risk by helping users interpret model outcomes properly.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Who is the primary target audience of the KoopaML platform?",{"text":80,"@type":76},"The platform targets non-expert users who want to learn and apply ML models to domain data in healthcare. It also supports expert users through extensible architecture.",{"name":82,"@type":73,"acceptedAnswer":83},"How does KoopaML help users build and run ML pipelines?",{"text":84,"@type":76},"KoopaML provides intuitive and educational graphical interfaces to define and execute ML pipelines. Its design focuses on understanding how algorithms work, interpreting outcomes, and enabling extension with new components, algorithms, or heuristics.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"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":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":28,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":28,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]