[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121272-en":3,"doc-seo-121272-105":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},121272,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",7,"Healthcare","Extending the Visual Data Exploration Loop towards Trustworthy Machine Learning in the Healthcare Domain","Integration of machine learning (ML) systems into healthcare settings creates novel opportunities, including pattern recognition in heterogeneous medical datasets, clinical decision support and process automation to save time, improve care quality, reduce costs, and relieve healthcare staff. Challenges include opaque systems, limited autonomy, and requirements for better communication, interaction, and human–machine decision-making. A core obstacle is the interprofessional gap between data scientists and healthcare professionals and the resulting lack of trust in ML models.","EuroVis Workshop on Visual Analytics (2024)  \nM. El-Assady and H.-J. Schulz (Editors)  \nExtending the Visual Data Exploration Loop towards Trustworthy Machine Learning in the Healthcare Domain  \nD. Antweiler 1,2 and G. Fuchs 1  \n1Fraunhofer IAIS, Sankt Augustin, Germany  \n2Fraunhofer Center for Machine Learning, Sankt Augustin, Germany  \nVA domain  \nknowlege knowledge  \nData  \nMachine Learning Model  \nVisual Analytics System  \nKnowledge  \nVisual Analytics  \nexpert  \nHealthcare professional  \nML knowledge  \nVA knowledge  \ngains  \nvisualizes  \ninteracts  \ndevelops & evaluates  \nis integrated into  \nis basis for  \nis probed by  \ndevelops & evaluates  \ngenerates artifacts  \ngenerates  \n2  \n3  \n|  Flow of Tacit Knowledge  Flow of Information  |  Flow of Trust\u003Cbr> |\n| --- | --- |\n\nFigure 1: Overview of the proposed visual analytics framework that fosters trust into healthcare machine learning. Circles correspond to  \nstages from the original framework. Rounded rectangles introduce the roles of domain experts along the process. Dotted arrows represent knowledge transfer, continuous arrows reference transfer of information, while violet wide arrows indicate flow of trust. Our focus lies specifically on the interprofessional gap, in which the VA expert acts as afacilitator for the multidisciplinary team (top violet arrows) .  \nAbstract  \nIntegration of machine learning (ML) systems into healthcare settings creates novel opportunities, including pattern recognition in heterogeneous medical datasets, clinical decision support as well as processes automation to save time, advance the quality of care, reduce costs and relieve healthcare staff. Challenges include opaque digital systems, curbed autonomy as well as require ments on communication, interaction and human-machine decision-making. Obstacles involve the interprofessional gap between data scientists and healthcare professionals (HCPs) during model development as well as the lack of trust into ML models. Visual Analytics (VA) enables versatile interactions between users and ML models via adaptable visualizations and has been success fully deployed to improve accuracy, identify bias and increase trust. However, specifically supporting HCPs to gain trust into ML models through VA systems is not sufficiently explored. We propose an extended visual data exploration framework towards  \ntrustworthy ML in the healthcare domain for multidisciplinary teams of data scientists, VA experts and HCPs. Additionally, we apply our framework to three real-world use cases for policy development, plausibility testing and model optimization.  \nCCS Concepts  \n• Applied computing → Health care information systems; • Computing methodologies → Machine learning;  \n© 2024 The Authors.  \nProceedings published by Eurographics-The European Association for Computer Graphics.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nDOI: 10.2312/eurova.20241107  \n2 of 6 D. Antweiler & G. Fuchs / Visual Data Exploration Loop for Trustworthy ML in Healthcare  \n1. Introduction  \nMachine learning (ML) applications in the healthcare domain are spreading steadily and ever quicker. Due to increasing patient counts, amount of data, as well as number and pace of decisions being made, both the needs and benefits of applying ML techniques are greater than ever. There are distinct opportunities for ML to reduce healthcare costs, decrease disease burden and increase positive outcomes for patients, as well as satisfaction of both patients and staff. Machine learning is therefore an important part of in the ongoing digital transformation of many healthcare systems worldwide.  \nWhile many applications for ML in healthcare have been proposed, only few have been successfully deployed in real-world clinical settings. Two main obstacles are the interprofessional gap between data scientists","cbCaigGfiEfEzbAs","https://ap.wps.com/l/cbCaigGfiEfEzbAs","pdf",404665,1,6,"English","en",105,"# Introduction\n# Related Work\n## Trust in Visual Analytics","[{\"question\":\"What motivates extending visual data exploration for trustworthy machine learning in healthcare?\",\"answer\":\"Healthcare ML promises major benefits, yet real deployments face barriers such as opaque systems, limited autonomy, and insufficient support for building trust during model development and decision-making.\"},{\"question\":\"What are the main challenges highlighted in the document?\",\"answer\":\"The document emphasizes the interprofessional gap between data scientists and healthcare professionals and the lack of trust in machine learning models, which disrupt communication and decision-making.\"},{\"question\":\"How does Visual Analytics contribute to trustworthy ML?\",\"answer\":\"Visual Analytics enables versatile interactions between users and ML models through adaptable visualizations, helping users connect their mental models with model outputs to improve accuracy, identify bias, and increase trust.\"}]","Extending the Visual Data Exploration Loop towards Trustworthy Machine Learning in the Healthcare Domain | 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motivates extending visual data exploration for trustworthy machine learning in healthcare?","Question",{"text":75,"@type":76},"Healthcare ML promises major benefits, yet real deployments face barriers such as opaque systems, limited autonomy, and insufficient support for building trust during model development and decision-making.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What are the main challenges highlighted in the document?",{"text":80,"@type":76},"The document emphasizes the interprofessional gap between data scientists and healthcare professionals and the lack of trust in machine learning models, which disrupt communication and decision-making.",{"name":82,"@type":73,"acceptedAnswer":83},"How does Visual Analytics contribute to trustworthy ML?",{"text":84,"@type":76},"Visual Analytics enables versatile interactions between users and ML models through adaptable visualizations, helping users connect their mental models with model outputs to improve accuracy, identify 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