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Obesity01:24

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The Body Mass Index (BMI) is a numerical value derived from a person's weight and height, used to categorize individuals into weight ranges. It is calculated using the formula: weight in kilograms divided by height in meters squared. Obesity is a health condition characterized by excessive accumulation of adipose tissue that poses health risks, often diagnosed with a BMI ≥ 30. This excess fat storage occurs when surplus dietary calories are converted into triglycerides and stored in...
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Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
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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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Considering the tensile strength of concrete involves recognizing that the theoretical strength of cement paste can be up to a thousand times higher than what is observed in practical applications. This significant discrepancy is largely attributed to the presence of microscopic cracks within the concrete. These cracks tend to amplify stress at their tips when a load is applied, a phenomenon explained by Griffith's theory of brittle fracture.
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使用机器学习预测儿童肥胖:实际考虑

Erika R Cheng1, Rai Steinhardt2, Zina Ben Miled3,4

  • 1Division of Children's Health Services Research, Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN 46202, USA.

BioMedInformatics
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概括
此摘要是机器生成的。

通过机器学习,预测儿童肥胖是可行的. 五次电子健康记录 (EHR) 接触足以准确预测早期儿童的体重指数 (BMI),使用长期短期记忆 (LSTM) 模型.

关键词:
这就是为什么BMI是BMI.欧洲人权理事会 欧洲人权理事会儿童肥胖 儿童肥胖 儿童肥胖机器学习是机器学习.

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科学领域:

  • 儿科健康 儿科健康
  • 机器学习在医学中的应用
  • 肥胖的预测方法

背景情况:

  • 机器学习显示出预测儿童肥胖的前景.
  • 现实世界的数据变化给现有的预测模型带来了挑战.
  • 准确的早期儿童体重指数 (BMI) 预测对于及时干预至关重要.

研究的目的:

  • 确定必要的电子健康记录 (EHR) 数据,以准确预测儿童BMI.
  • 开发和验证用于早期儿童BMI估计的机器学习模型.
  • 确定有效预测幼儿BMI的关键变量.

主要方法:

  • 使用了0-4岁儿童的纵向数据集.
  • 开发了长期短期记忆 (LSTM) 循环神经网络模型,使用来自2-8次临床接触的EHR数据.
  • 使用K折交叉验证,平均平均误差 (MAE) 和皮尔森相关系数 (R2) 评估的模型.

主要成果:

  • 五次EHR遇到足以准确预测BMI (MAE=0.98,R2=0.72).
  • 综合性别分层模型的表现优于单个性别分层模型.
  • 将269个暴露变量减少到BMI估计的24个关键预测因素.

结论:

  • 五次临床接触提供了足够的数据来预测儿童早期的BMI.
  • 在LSTM模型中,使用有限的关键变量组,可以准确估计BMI.
  • 该研究确定了未来儿童肥胖预测模型的基本变量.