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Freezing Point Depression and Boiling Point Elevation03:12

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Vertical curves are essential in roadway design because they provide smooth transitions between varying roadway grades. Designing vertical curves involves calculating intermediate elevations and identifying the curve's highest or lowest point, which is essential for optimal roadway performance.Intermediate elevations on a vertical curve are determined using the tangent offset method. This method considers the initial elevation at the start of the curve, the grades, and the curve's geometry. The...
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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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概括
此摘要是机器生成的。

机器学习模型可以使用常规数据识别具有高脂蛋白 (Lp) 的个体. 这些人工智能策略提高了查效率和对动脉样硬化心血管疾病风险评估的公平性.

关键词:
电子健康记录是电子健康记录.实施实施实施实施实施.脂蛋白 (a) 是一种蛋白质机器学习是机器学习.查检查 查检查 查检查 查检查

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科学领域:

  • 心血管医学 心血管医学
  • 人工智能的人工智能
  • 遗传学 遗传学 是一个

背景情况:

  • 脂蛋白 (Lp) 是一种由基因决定的,对动脉样硬化心血管疾病 (ASCVD) 的终身风险因素.
  • 尽管指南建议进行普遍测试,但在临床实践中很少进行Lp (a) 测量.
  • 需要有效和公平的方法来选升高的Lp.

研究的目的:

  • 审查最近在机器学习 (ML) 基于LP (a) 查的策略方面的进展.
  • 突出ML如何提高识别Lp升高个体的效率,收益率和公平性.

主要方法:

  • 对开发和验证ML模型用于Lp (a) 识别的研究的审查.
  • 使用常规可用的临床变量分析ML模型.
  • 检查像ARISE这样的框架及其在不同群体中的验证.

主要成果:

  • 三项研究已经开发并验证了ML模型以识别患有升高Lp的个体,使用常规临床数据.
  • 在ARISE框架下,测试所需的人数减少了50%以上,并且在各个人口亚组中表现一致.
  • 决策树和神经网络模型证明了在临床和人口环境中改善病例发现的可行性.

结论:

  • 基于ML的策略提供了一个可扩展的方法来实施普遍的Lp (a) 测试建议.
  • 当使用无偏见的数据开发,外部验证,并对公平性进行评估时,ML模型可以系统地识别具有高Lp的个体.
  • 这些模型可以促进将Lp(a) 测量纳入常规心血管风险评估.