[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123412-en":3,"doc-seo-123412-105":30,"detail-sidebar-cat-0-en-105":83},{"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},123412,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Machine Learning Approaches in Remote Patient Monitoring and Healthcare QA","This paper presents an intelligent remote patient monitoring (RPM) system that integrates machine learning for predictive alerting and quality assurance (QA). The framework extends RPM architectures by embedding anomaly detection algorithms, patient-specific risk models, and test case prioritization for clinical QA. Evaluation on simulated patient data shows improvements in early warning accuracy, reductions in QA cycle time, and stronger compliance with healthcare software quality standards. Results emphasize the adaptability and impact of AI-enabled predictive and QA mechanisms on safer patient outcomes.","Machine Learning Approaches in Remote Patient Monitoring and Healthcare QA  \nDr. Raju Dindigala ¹, Aminul Islam Rana²  \nProfessor & Head Department of Mathematics, JB Institute of Engineering & Technology, India1, Assistant Professor / Research Lead, Regent College London2,  \n[20122102india@gmail.com](20122102india@gmail.com1)[1](20122102india@gmail.com1), [mxi349@bham.ac.uk](mxi349@bham.ac.uk2)[2](mxi349@bham.ac.uk2)  \nDOI: 10.69987/JACS.2024.40203  \n\n| K ey w o r d s |  | A b s t r a c t |\n| --- | --- | --- |\n| Machine learning, remote patient monitoring, healthcare quality assurance, predictive analytics, healthcare optimization. |  | This paper presents an intelligent remote patient monitoring (RPM) system that integrates machine learning for predictive alerting and quality assurance (QA) . The proposed framework builds on established principles of leveraging AI and IoT technologies to enhance RPM for healthcare systems. It extends prior system architectures by embedding anomaly detection algorithms, patientspecific risk models, and test case prioritization methods for clinical QA. The system was evaluated using simulated patient data, demonstrating significant improvements in early warning accuracy, reductions in QA cycle times, and enhanced compliance with healthcare software quality standards. These findings highlight the adaptability and impact of advanced AI-enabled frameworks in transforming healthcare infrastructure, reinforcing the importance of integrating predictive and QA mechanisms for improved patient outcomes. |\n\nIntroduction  \nThe demand for scalable and secure remote healthcare monitoring systems has grown exponentially with the global expansion of telehealth services, particularly in response to increased patient loads and the need for continuous care beyond traditional clinical settings. In their influential 2023 study, Kothamali et al. introduced a hybrid Artificial Intelligence–Internet of Things (AIIoT) Remote Patient Monitoring (RPM) framework designed to enable real-time diagnostics, facilitate patient engagement, and support healthcare professionals with timely, data-driven insights. Their model served as a foundational baseline for developing intelligent systems capable of reducing medical errors, enhancing clinical decision-making, and improving overall patient outcomes.  \nBuilding upon this groundwork, the present paper adapts and extends Kothamali et al.’s framework by incorporating advanced machine learning components to automate alert management, detect anomalies, and prioritize patient conditions based on risk levels. Moreover, the enhanced system integrates software quality assurance (SQA) protocols to ensure reliability, data integrity, and compliance with healthcare standards. This evolution not only supports continuous patient monitoring with minimal clinician intervention but also addresses key challenges in system accuracy,  \nscalability, and cybersecurity—critical for delivering safe and efficient telehealth services at scale.  \nLiterature Review  \nExisting Remote Patient Monitoring (RPM) systems often rely on static, rule-based alert mechanisms that fail to account for the complex and dynamic nature of individual patient health patterns. These rigid systems typically lack the adaptability required to accommodate patient-specific variability, leading to false positives, overlooked anomalies, and increased clinician workload. Addressing these limitations, Kothamali et al.(2023) made a significant contribution by introducing a hybrid AI-IoT framework that integrates predictive analytics into healthcare delivery. Their work marked a pivotal shift toward intelligent, context-aware monitoring systems capable of enhancing real-time decision-making and improving patient engagement.  \nBuilding upon this foundation, our study advances the integration of Artificial Intelligence in RPM by incorporating machine learning models specifically designed for abnormal pattern detection across diverse pat","cbCaiklYoVqXjs0a","https://ap.wps.com/l/cbCaiklYoVqXjs0a","pdf",317117,1,7,"English","en",105,"# Introduction\n# Literature Review\n# Methodology\n## Patient-Specific Health Profiling","[{\"question\":\"How was the approach evaluated, and what were the key outcomes?\",\"answer\":\"The system was evaluated using simulated patient data. Findings indicate better early warning accuracy, shorter QA cycle times, and improved adherence to healthcare software quality standards.\"}]","Machine Learning Approaches in Remote Patient Monitoring and Healthcare QA | PDF",1785816348,18,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"machine-learning-approaches-in-remote-patient-monitoring-and-healthcare-qa","",{"@graph":36,"@context":77},[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/machine-learning-approaches-in-remote-patient-monitoring-and-healthcare-qa/123412/",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","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"How was the approach evaluated, and what were the key outcomes?","Question",{"text":75,"@type":76},"The system was evaluated using simulated patient data. 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