支持物联网的生物传感器,用于实时监测和早期发现慢性疾病。

Physical activity and nutrition Pub Date : 2024-12-01 Epub Date: 2024-12-31 DOI:10.20463/pan.2024.0033
Md Najmul Hosain, Yi-Sub Kwak, Jaeeun Lee, Hongseok Choi, Jungwon Park, Jongnam Kim
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引用次数: 0

摘要

目的:本研究的主要目的是检查和强调将物联网(IoT)技术和生物传感器集成在医疗保健领域的重大影响,重点关注它们推动医疗保健实质性进步和改进的潜力。重点是解决慢性病带来的全球挑战,提出一个全面的卫生保健系统,促进这些疾病的实时监测、早期发现和远程管理。慢性疾病以其持续时间长、进展缓慢为特点,已成为全球卫生保健系统面临的一个显著挑战。本文旨在说明具有识别特定生物标志物能力的生物传感器如何在提供个性化患者护理,提高结果和减轻医疗费用方面发挥关键作用。方法:采用系统、全面的方法分析物联网(IoT)技术与生物传感器的融合在慢性疾病实时监测和早期发现中的应用。相关文献来源于知名数据库,包括IEEE explore、PubMed和爱思唯尔的ScienceDirect,重点关注2014年至2024年间发表的研究。“医疗物联网”、“慢性病生物传感器”和“实时监测系统”等关键词指导了选择过程。本综述包括原创研究文章、综述论文和案例研究,对其进行了批判性分析,以评估该跨学科领域的当前进展、挑战和未来方向。综合研究结果,深入了解物联网生物传感器如何改变医疗保健,特别是慢性病管理。结果:本研究探索了物联网与生物传感器的融合,用于慢性疾病的实时监测。这种组合提供个性化的医疗保健、早期检测和降低成本。应用包括远程病人监护、心脏健康、血糖管理和老年人护理。尽管面临挑战,但持续的进步有望优化准确性、效率和道德合理性,迎来以患者为中心的医疗保健时代。结论:整合物联网生物传感器解决慢性病带来的全球挑战。这项研究通过促进实时监测、早期检测和个性化护理,强调了这种融合在医疗保健领域的潜力。通过超越传统监测系统的局限性,物联网生物传感器提供了对患者病情的持续洞察;健康,使主动干预成为可能。它们在远程监测、心脏健康、血糖管理和老年护理等多个领域得到了应用,展示了它们在推进精准医疗和改善患者预后方面的作用。尽管存在技术障碍,但小型化、边缘计算和人工智能驱动的分析方面的持续进步旨在提高准确性、效率和道德实践,为积极主动、以患者为中心的医疗保健时代铺平道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IoT-enabled biosensors for real-time monitoring and early detection of chronic diseases.

Purpose: The main objective of this study is to examine and highlight the substantial impact of integrating Internet of Things (IoT) technology and biosensors in the healthcare sector, focusing on their potential to drive substantial advancements and improvements in healthcare. Emphasis is placed on tackling the global challenge posed by chronic diseases by proposing an all-encompassing healthcare system that facilitates real-time monitoring, early detection, and remote management of these conditions. Chronic diseases, distinguished by their prolonged duration and gradual progression, have emerged as a marked challenge for healthcare systems worldwide. This paper seeks to illustrate how biosensors, with the capability to identify specific biomarkers, can play a pivotal role in delivering personalized patient care, enhancing outcomes, and mitigating healthcare expenses.

Methods: This review was conducted using a systematic and comprehensive approach to analyze the integration of Internet of Things (IoT) technology with biosensors for real-time monitoring and early detection of chronic diseases. Relevant literature was sourced from reputable databases, including IEEE Xplore, PubMed, and Elsevier's ScienceDirect, focusing on studies published between 2014 and 2024. Keywords such as "IoT in healthcare," "biosensors for chronic diseases," and "real-time monitoring systems" guided the selection process. This review included original research articles, review papers, and case studies, which were critically analyzed to assess current advancements, challenges, and future directions in this interdisciplinary field. The findings were synthesized to provide an in-depth understanding of how IoT-enabled biosensors are transforming healthcare, particularly in chronic disease management.

Results: This research explores the integration of IoT and biosensors for real-time monitoring of chronic diseases. The combination offers personalized healthcare, early detection, and cost reduction. Applications include remote patient monitoring, cardiac health, glucose management, and elderly care. Despite challenges, ongoing advancements promise to optimize accuracy, efficiency, and ethical soundness, ushering in a patient-centric healthcare era.

Conclusion: The integration of IoT-enabled biosensors approach to addressing global challenges posed by chronic diseases. This study highlights the potential of this convergence in healthcare by facilitating real-time monitoring, early detection, and personalized care. By surpassing limitations of traditional monitoring systems, IoT-enabled biosensors provide continuous insights into patients' health, enabling proactive interventions. Their applications are demonstrated in diverse domains, including remote monitoring, cardiac health, glucose management, and elderly care, showcasing their role in advancing precision medicine and improving patient outcomes. Despite technical hurdles, ongoing advancements in miniaturization, edge computing, and AI-driven analytics aim to enhance accuracy, efficiency, and ethical practices, paving the way for a proactive and patient-centric healthcare era.

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