[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120884-en":3,"doc-seo-120884-105":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},120884,3848291630094,"Emma Wilson","https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals - Paper Abstract","Occupational stress significantly affects workers in healthcare, arising from mismatches between work conditions, employees’ capability to perform tasks, and the social support available from colleagues and management. The study evaluates multiple machine learning algorithms to determine whether explanation methods improve objective stress identification using statistical biomarker data from the AffectiveRoad database. Explainability is integrated to support detection across stress levels, with Random Forest performing best, followed by k-Nearest Neighbors and Neural Network.","King’s Research Portal  \nDocument Version  \nPeer reviewed version  \nLink to publication record in King's Research Portal  \nCitation for published version (APA):  \nSeibert Fernandes, M. , Rodrigues Filho, R. , Sene-Junior, I. , Sarkadi, S. , Panisson, A. R. , & Schiaffino Morales, A. (Accepted/In press) . An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals. In 16th International Conference on Agents and Artificial Intelligence (ICAART 2024) SciTePress.  \nCiting this paper  \nPlease note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. 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May. 2024  \nAn Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals  \nMilena Seibert Fernandes 1 , Roberto Rodrigues Filho 1 , Iwens Sene-Junior2 , Stefan Sarkadi3 ,  \nAlison R. Panisson 1 and Anal´ucia Schiaffino Morales 1  \n1 Department of Computing, Federal University of Santa Catarina, Santa Catarina, Brazil  \n2 Institute of Informatics, Federal University of Goi a´s, Goiaˆnia, Brazil  \n3 Department of Informatics, King’s College London, London, U.K.  \n[iwens@ufg.br](iwens@ufg.br), [stefan.sarkadi@kcl.ac.uk](stefan.sarkadi@kcl.ac.uk),{analucia.morales, [alison.panisson](alison.panisson}@ufsc.br)[}](alison.panisson}@ufsc.br)[@ufsc.br](alison.panisson}@ufsc.br)  \nKeywords: Interpretable ML, Stress, AI for Healthcare.  \nAbstract: In the last few years, several scientific studies have shown that occupational stress has a significant impact on workers, particularly those in the healthcare sector. This stress is caused by an imbalance between work conditions, the worker’s ability to perform their tasks, and the social support they receive from colleagues and management professionals. Researchers have explored occupational stress as part of a broader study on affective systems in healthcare, investigating the use of biomarkers and machine learning approaches to identify early conditions and avoid Burnout Syndrome. In this paper, a set of machine learning (ML) algorithms was evaluated using statistical data on biomarkers from the AffectiveRoad database to determine whether the use of explanations can help identify stress more objectively. This research integrates explainability and machine learning to aid in the identification of various levels of stress, which has not been previously evaluated for the domain of occupational stress. The Random Forest is the best-performing model for this assignment, followed by k-Nearest Neighbors and Neural Network. Later, explainers were applied to the Random Forest, highlighting feature importance, partial dependencies between characteristics, and a summary of the impact of ","cbCaicXAaFoQnLvb","https://ap.wps.com/l/cbCaicXAaFoQnLvb","pdf",351769,1,9,"English","en",105,"# Abstract\n# Introduction\n## Occupational stress in healthcare\n## Questionnaire-based identification and its bias\n## Sensor-based biomarkers and machine learning","[{\"question\":\"What causes occupational stress in healthcare professionals according to the document?\",\"answer\":\"Occupational stress is attributed to an imbalance between work conditions, the worker’s ability to perform tasks, and the social support received from colleagues and management.\"},{\"question\":\"How does the paper identify occupational stress?\",\"answer\":\"It evaluates machine learning algorithms using statistical biomarker data from the AffectiveRoad database and applies explainability methods to interpret model outputs.\"},{\"question\":\"Which model performed best and how were explanations used?\",\"answer\":\"Random Forest achieved the best performance. Explanations were applied to highlight feature importance, partial dependencies between characteristics, and how feature values impact outputs.\"}]","An Interpretable Machine Learning Approach for Identifying Occupational Stress in Healthcare Professionals - Paper Abstract | PDF",1785732489,23,{"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},"an-interpretable-machine-learning-approach-for-identifying-occupational-stress-in-healthcare-professionals-paper-abstract","",{"@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/an-interpretable-machine-learning-approach-for-identifying-occupational-stress-in-healthcare-professionals-paper-abstract/120884/",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","2026-08-03",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 causes occupational stress in healthcare professionals according to the document?","Question",{"text":75,"@type":76},"Occupational stress is attributed to an imbalance between work conditions, the worker’s ability to perform tasks, and the social support received from colleagues and management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the paper identify occupational stress?",{"text":80,"@type":76},"It evaluates machine learning algorithms using statistical biomarker data from the AffectiveRoad database and applies explainability methods to interpret model outputs.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and how were explanations used?",{"text":84,"@type":76},"Random Forest achieved the best performance. 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