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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
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基于人工智能和整体分类的疾病诊断.

Asmaa H Rabie1, Ahmed I Saleh1

  • 1Computer Engineering and Systems Dept., Faculty of Engineering, Mansoura University, Mansoura, Egypt.

Artificial intelligence in medicine
|February 7, 2024
PubMed
概括

本研究介绍了一种使用机器学习的新计算机辅助疾病诊断 (CAD2) 策略. CAD2系统显著提高了诊断准确度,减少了错误,为医疗决策提供了更有效的方法.

科学领域:

  • 医疗信息学 医疗信息学
  • 人工智能在医学中的应用
  • 机器学习应用 机器学习应用

背景情况:

  • 计算机辅助诊断 (CAD) 对于增强医疗诊断系统至关重要.
  • 将人工智能 (AI) 集成到CAD系统中,为自动化和减少错误提供了巨大的潜力.
  • 医学诊断中的传统手动工作流程往往是缓慢的,不准确的,容易出现人为错误.

研究的目的:

  • 通过机器学习 (ML) 提出一个全面的计算机辅助疾病诊断 (CAD2) 策略.
  • 通过增强的诊断方法,帮助临床医生做出更知情,更准确的医疗决策.

主要方法:

  • 拟议的CAD2战略包括三个连续的阶段:异常值拒绝阶段 (ORP),特征选择阶段 (FSP) 和分类阶段 (CP).
  • ORP采用了一种新的异常值拒绝技术 (ORT),具有快速异常值拒绝 (FOR) 和准确异常值拒绝 (AOR) 阶段.
  • FSP使用混合选择技术 (HST) 结合渔民得分 (QS2) 和混合生物灵感优化 (HBO) 技术 (PS2). CP依赖于集成分类技术 (ECT).

主要成果:

  • 在两个数据集 (一般疾病和COVID-19) 上的实验验证证明了CAD2策略的高效性.
  • 拟议的CAD2系统在准确性,错误率,精度和回忆方面超过了现有的诊断策略.
关键词:
计算机辅助诊断是指计算机辅助诊断.诊断 诊断 诊断 诊断 诊断疾病 疾病 疾病整体分类的统一分类.功能选择 功能选择异常值的拒绝情况

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  • 统计测试 (Wilcoxon签名排名和弗里德曼) 证实了CAD2.2的优越性能.
  • 结论:

    • 结合ORP,FSP和CP的CAD2策略提供了准确的疾病诊断.
    • 与替代诊断方法相比,它实现了更高的准确性,更低的错误率和更短的实施时间.