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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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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 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
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
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Updated: Jul 6, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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使用机器学习算法预测未出席预约的决策分析框架.

Carolina Deina1, Flavio S Fogliatto2, Giovani J C da Silveira3

  • 1Department of Industrial Engineering, Federal University of Rio Grande do Sul, Av. Osvaldo Aranha, 99, 5° Andar, Porto Alegre, 90035-190, Brazil. caroldeina@gmail.com.

BMC health services research
|January 5, 2024
PubMed
概括

这项研究引入了符号回归 (SR) 和实例硬度值 (IHT) 来预测患者不出现,优于现有方法. 这种新的方法确保了对医疗保健资源优化的强大模型概括.

关键词:
分类算法的分类算法.医疗保健环境 医疗环境一个不平衡的数据集.机器学习 机器学习错过的预约 错过的预约重新采样技术重新采样技术

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科学领域:

  • 医疗保健分析 医疗保健分析
  • 机器学习 机器学习
  • 数据科学数据科学数据科学

背景情况:

  • 患者不出现会对医疗保健系统和患者的治疗结果产生负面影响.
  • 机器学习提供了一个解决方案,可以预测没有出现的情况,从而实现主动的资源管理.

研究的目的:

  • 开发和评估一个新的框架来预测患者没有出现,特别是解决不平衡的数据集.
  • 引入和评估符号回归 (SR) 和实例硬度值 (IHT) 进行不显示预测.

主要方法:

  • 提出了一个框架,包括一种新的双z折交叉验证.
  • 符号回归 (SR) 和实例硬度值 (IHT) 与KNN,SVM,RUS,SMOTE和NearMiss-1进行了比较.
  • 该框架在两个巴西医院的数据集上得到了验证,其不出现率各不相同.

主要成果:

  • 符号回归 (SR) 和实例硬度值 (IHT) 在预测患者不出现方面表现出卓越的表现.
  • 实例硬度值 (IHT) 在所有测试的分类算法中表现出色,性能变化低.
  • 该研究取得了高灵敏度结果,在两个数据集上都超过了0.94,超过了现有的文献.

结论:

  • 这项研究是首次将SR和IHT应用于患者无病预测,并实施双z交叉验证.
  • 这些发现强调了由于在不平衡的数据集上运行不够的验证而导致偏差结果的风险.
  • 拟议的框架增强了模型概括和稳定性分析,以准确地预测不显示的情况.