[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123189-en":3,"doc-seo-123189-105":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},123189,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management - Article","Predictive modeling for healthcare worker well-being uses cloud computing and machine learning to manage stress proactively. The approach addresses rising demands on healthcare staff and the need to track mental health with actionable early detection. Data from surveys, social media, electronic health records, and wearable devices is aggregated for analysis, then gradient boosting models predict stress levels and overall well-being. The scalable cloud infrastructure enables real-time monitoring and facilitates targeted interventions to reduce stress patterns and improve employee health.","Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management  \nMuthukathan Rajendran Sudha1, Gnanamuthu Bai Hema Malini2, Rangasamy Sankar3, Murugaaboopathy Mythily4, Piskala Sathiyamurthy Kumaresh5, Mageshkumar Naarayanasamy Varadarajan6, Shanmugam Sujatha7  \n1Department of Computer Applications, College of Science and Humanities, SRM Institute of Science and Technology, Chennai, India 2Department of Computer Science, Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, Chennai, India 3Department of Electrical and Electronics Engineering, Chennai Institute of Technology, Chennai, India 4Division of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India 5Department of Electronics and Communication Engineering, K.L.N. College of Engineering, Sivagangai, India  \n6Lead Software Engineer, Glen Allen, USA  \n7Department of Biomedical Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences,  \nSaveetha University, Chennai, India  \nArticle history:  \nReceived Jun 15, 2024 Revised Sep 6, 2024 Accepted Oct 1, 2024  \nKeywords:  \nCloud computing Machine learning Mental health Physiological data Stress management  \nCorresponding Author:  \nThis paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being.  \nThis is an open access article under the CC BY-SA license.  \nMuthukathan Rajendran Sudha  \nDepartment of Computer Applications, College of Science and Humanities, SRM Institute of Science and Technology  \nPotheri, SRM Nagar, Kattankulathur, Tamil Nadu 603203, India  \n[Email: mrsudha51@gmail.com](Email: mrsudha51@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the coronavirus disease 2019 (COVID-19) epidemic, nurses are stressed. Over time, this tremendous pressure affects their health, quality of life, and patient care. Real-time stress detection and monitoring are crucial for early stress pattern diagnosis, burnout avoidance, and better patient-care outcomesin healthcare personnel [1] . Our proof-of-concept case study uses machine learning (ML) and artificial intelligence (AI) to estimate user stress levels based on heart rate, variability, and physical activity. This research explores Norwegian hospital workers' stress and cybersecurity activities. Hospital data is more  \nsusceptible to cyber assaults as the healthcare industry leverages technology to enhance patient care, and hackers may exploit the human component [2] . In India, the second COVID-19 epidemic has caused drug scarcity and increased morbidity. Due to the epidemic's suffering, mortality, and seclusion, COVID-19 has also affected health practitioners' mental health [3] . This cross-sectional ","cbCaiiciVtTIh0pd","https://ap.wps.com/l/cbCaiiciVtTIh0pd","pdf",364329,1,11,"English","en",105,"# Article Info\n## Abstract\n## Introduction\n## Methods and Approach\n## System Design and Data Sources\n## Predictive Modeling with Gradient Boosting\n## Cloud Computing for Real-Time Monitoring\n## Impact and Use Cases\n## Open Access License","[{\"question\":\"What problem does the predictive modeling approach target?\",\"answer\":\"It targets proactive stress management for healthcare workers by predicting their stress levels and well-being to support earlier identification of stress patterns and burnout risk.\"},{\"question\":\"Which data sources are used in the system?\",\"answer\":\"The system compiles information from surveys, social media, electronic health records, and wearable devices, aggregating them into a single location for analysis.\"},{\"question\":\"Why is gradient boosting selected for the predictive models?\",\"answer\":\"Gradient boosting is chosen for its resilience to overfitting and its ability to process many kinds of data effectively.\"}]","Predictive modeling for healthcare worker well-being with cloud computing and machine learning for stress management - 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