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相关概念视频

Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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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
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

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A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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相关实验视频

Updated: May 21, 2025

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评估和实施机器学习模型,以提供个性化的移动健康应用程序建议.

Hafsat Morenigbade1, Tareq Al Jaber2, Neil Gordon2

  • 1Centre of Excellence for Data Science, AI and Modelling, Faculty of Science and Engineering, University of Hull, United Kingdom.

PloS one
|March 19, 2025
PubMed
概括

这项研究使用机器学习开发了一个医疗保健应用程序推系统. 伯特模型的准确度达到了90%左右,为用户改善了移动健康 (mHealth) 应用程序的发现.

科学领域:

  • 数字健康数字健康
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 移动健康 (mHealth) 应用程序的普及需要有效的评估和推系统.
  • 越来越多的mHealth市场强调了需要工具来引导用户到相关的健康应用程序.

研究的目的:

  • 为移动健康应用设计和评估推系统.
  • 为了利用应用程序属性,特别是描述,进行上下文用户建议.
  • 为了改善用户体验,对健康应用进行分类.

主要方法:

  • 一个健康应用程序属性的数据集,包括描述,评分和评论,被策划.
  • 数据预处理涉及一次性编码,标准化和功能工程,包括一个新的"评分_评论"功能.
  • 机器学习和深度学习模型,包括随机森林和BERT,使用"类别" (例如"减肥"",医疗") 作为目标变量进行评估.

主要成果:

  • 采用转移学习的BERT模型表现出高效率,在超参数调整后达到约90%的准确性.
  • 功能工程,包括"评分_评论"指标,有助于模型性能.
  • "类别"变量有效地区分了mHealth景观中的不同健康背景.

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结论:

  • 转移学习,特别是与BERT模型,是非常有效的mHealth应用推.
  • 使用应用程序描述和以用户为中心的功能,可以构建一个强大的推系统.
  • 开发的系统采用了共弦相似性,根据用户查询相关性准确排名应用程序.