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相关概念视频

Three Developmental Domains01:29

Three Developmental Domains

Human development is typically examined across three main domains: physical, cognitive, and socio-emotional. These domains represent the significant areas of change and continuity throughout the lifespan, from infancy to late adulthood.
Physical Development
Physical processes, also known as maturation, encompass the biological changes that occur across an individual's life. These changes begin with genetic inheritance and continue through various stages, including growth in height and weight,...

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A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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使用机器学习技术检测儿科发育迟缓

Shin-Bo Chen1, Chi-Hung Huang2, Sheng-Chin Weng2

  • 1Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei City, Taiwan (R.O.C.

PloS one
|May 20, 2025
PubMed
概括

预测儿童发育迟缓 (DD) 是非常重要的. 这项研究表明,疗法频率可以使用机器学习准确预测DD,为早期干预提供一个具有成本效益的查工具.

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科学领域:

  • 儿科医学 儿科医学
  • 医疗保健中的机器学习
  • 发育儿科 发育儿科

背景情况:

  • 早期识别发育迟缓 (DD) 的儿童对于通过及时干预来改善结果至关重要.
  • 目前的诊断方法可能是昂贵和先进的,突出了需要可访问的查工具.
  • 治疗访问频率代表了预测DD的潜在低成本数据源.

研究的目的:

  • 为了研究使用治疗访问频率 (身体,职业,语言治疗) 来预测儿童发育迟缓的有效性.
  • 根据历史治疗数据开发和评估用于预测DD的机器学习模型.
  • 建立一种具有成本效益的查方法,用于查处于发育迟缓风险的儿童.

主要方法:

  • 利用了2,552名门诊患者的数据集,其中来自台湾医院的34,862次访问 (2012-2016).
  • 开发并比较了三种机器学习模型:深度神经网络 (DNN),支持矢量机器 (SVM) 和决策树 (DT).
  • 使用F1得分,灵敏度和正预测值评估模型性能.

主要成果:

  • 决策树 (DT) 模型表现出卓越的性能,特别是当优先考虑高灵敏度时.
  • 性能最好的DT模型实现了0.902的灵敏度和0.723.72的正预测值.
  • 根据治疗频率,DT模型在预测发育延迟方面表现优于DNN和SVM模型.

结论:

  • 治疗访问频率是儿童发育迟缓的有价值预测指标.
  • 机器学习模型,特别是决策树,可以有效地利用这些易于获得的数据预测DD.
  • 这些具有成本效益的预测模型在早期DD查和干预中具有广泛临床应用的巨大潜力.