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<article xlink="http://www.w3.org/1999/xlink" dtd-version="1.0" article-type="nursing" lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher">JOHS</journal-id>
      <journal-id journal-id-type="nlm-ta">Journ of Health Scien</journal-id>
      <journal-title-group>
        <journal-title>Journal of HealthCare Sciences</journal-title>
        <abbrev-journal-title abbrev-type="pubmed">Journ of Health Scien</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">2231-2196</issn>
      <issn pub-type="opub">0975-5241</issn>
      <publisher>
        <publisher-name>Radiance Research Academy</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">375</article-id>
      <article-id pub-id-type="doi">http://dx.doi.org/10.52533/JOHS.2024.41254</article-id>
      <article-id pub-id-type="doi-url"/>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Nursing</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>The Value of Vital Sign Changes for Early Detection of Clinical Deterioration&#13;
</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Althmali</surname>
            <given-names>Majed Ahmad</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Almalki</surname>
            <given-names>Waleed Maid</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Alzahrani</surname>
            <given-names>Mohammed Saeed</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Al-Tuwairqi</surname>
            <given-names>Sati Khalaf Allah</given-names>
          </name>
        </contrib>
        <contrib contrib-type="author">
          <name>
            <surname>Althubity</surname>
            <given-names>Fahd Saad</given-names>
          </name>
        </contrib>
      </contrib-group>
      <pub-date pub-type="ppub">
        <day>31</day>
        <month>12</month>
        <year>2024</year>
      </pub-date>
      <volume>4</volume>
      <issue>12</issue>
      <fpage>1044</fpage>
      <lpage>1050</lpage>
      <permissions>
        <copyright-statement>This article is copyright of Popeye Publishing, 2009</copyright-statement>
        <copyright-year>2009</copyright-year>
        <license license-type="open-access" href="http://creativecommons.org/licenses/by/4.0/">
          <license-p>This is an open-access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0) Licence. You may share and adapt the material, but must give appropriate credit to the source, provide a link to the licence, and indicate if changes were made.</license-p>
        </license>
      </permissions>
      <abstract>
        <p>Advancements in vital sign monitoring have transformed the detection and management of clinical deterioration, providing opportunities for earlier intervention and improved patient outcomes. Vital signs, including heart rate, respiratory rate, blood pressure, and oxygen saturation, often show subtle changes before critical conditions develop. The integration of continuous monitoring technologies, wearable devices, and predictive algorithms has enhanced the ability to track and interpret these changes. Machine learning models, such as recurrent neural networks, have demonstrated significant promise in identifying patterns within vital sign trends that may go unnoticed in traditional assessments. Despite these advancements, challenges persist in optimizing the use of vital sign changes for early detection. Variability in baseline physiological parameters across diverse populations complicates the establishment of universal thresholds, increasing the risk of false alarms or missed detections. Data quality issues, stemming from motion artifacts and inconsistent sensor performance, further hinder reliability. Resource disparities exacerbate these challenges, as under-resourced healthcare settings often lack access to advanced monitoring systems. Emerging technologies, such as wireless body area networks and cloud-based platforms, have enhanced the scalability and effectiveness of monitoring solutions. These tools facilitate real-time data analysis and provide actionable insights, even in remote or resource-constrained environments. However, ethical concerns related to data privacy and security remain critical considerations in the widespread adoption of these systems. The future of vital sign monitoring lies in addressing these limitations through interdisciplinary collaboration and innovative solutions. Standardizing data collection methods, refining predictive algorithms, and developing affordable, accessible technologies are essential steps. With sustained efforts, vital sign monitoring has the potential to revolutionize healthcare delivery, enabling more proactive and equitable patient care.&#13;
</p>
      </abstract>
      <kwd-group>
        <kwd>Vital signs</kwd>
        <kwd> early detection</kwd>
        <kwd> clinical deterioration</kwd>
        <kwd> predictive monitoring</kwd>
        <kwd> healthcare technology</kwd>
      </kwd-group>
    </article-meta>
  </front>
</article>