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相关概念视频

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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使用机器学习识别死产的风险.

Tess E K Cersonsky1, Nina K Ayala1, Halit Pinar2

  • 1Department of Obstetrics & Gynecology, Women & Infants Hospital of Rhode Island, Warren Alpert Medical School of Brown University, Providence, RI.

American journal of obstetrics and gynecology
|June 14, 2023
PubMed
概括

机器学习模型使用妊娠数据准确预测死胎风险,在生命能力之前识别了85%的病例. 这些先进的工具为更好的临床决策和预防死产提供了改进的风险分层.

关键词:
推动了树木的生长.在临床决策过程中.在因子分析方面,我们进行了因素分析.母体血清α-fetoprotein) 的使用情况.产前护理 产前护理可预测性的可预见性.随机的森林随机的森林第二个三个月的产前查 (唐氏综合征风险)结构性种族主义是结构性种族主义.超声波超声波是指超声波的使用.没有结合的雌醇.

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科学领域:

  • 围产儿医学 围产儿医学
  • 医疗保健中的机器学习
  • 预测分析是一种预测分析.

背景情况:

  • 传统的逻辑回归模型用于死胎预测缺乏捕捉复杂,非线性关系的复杂性.
  • 先进的机器学习 (ML) 方法为模拟变量和死产结果之间的复杂关联提供了卓越的能力.

研究的目的:

  • 开发和优化ML模型,以利用怀孕早期 (22-24周) 和整个妊娠期间可用的数据来预测死胎.
  • 确定导致死产风险的主要人口,医疗和产前因素,包括超声波和胎儿遗传学,这些因素有助于死产风险.

主要方法:

  • 对死产协作研究网络 (SCRN) 数据集 (2006-2009) 的二次分析.
  • 开发随机森林模型,使用可用数据,在生命力之前和整个怀孕期间.
  • 识别和评估为预测死胎的重要性变量.

主要成果:

  • 使用可预测性数据的随机森林模型实现了85.1%的准确性,具有高灵敏度 (88.6%) 和特异性 (85.3%).
  • 一个包含整个怀孕期间数据的模型显示准确率为85.0%,灵敏度为92.2%,特异性为77.9%.
  • 关键预测因素包括先前的死胎,少数民族种族,访问/超声波时的早期妊娠年龄和第二个三个月的查.

结论:

  • 复杂的ML模型可以使用全面的临床数据准确预测生育前死产风险 (85%).
  • 经过验证的ML模型具有有效风险分层和临床决策支持在识别和监测高风险怀孕的潜力.