Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
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...
6.3K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Perinatal outcomes associated with antiretroviral therapy for pregnant women living with HIV: an umbrella review.

EClinicalMedicine·2026
Same author

Global and regional molecular epidemiology of HIV-1 during 1990-2024: systematic review, global survey, and analysis of prevalence.

The Lancet. Infectious diseases·2026
Same author

Comparative evaluation of oxidative stress biomarkers F2-isoprostanes and 8-OHdG in Parkinson's disease and Type 2 Diabetes Mellitus: a systematic review and meta-analysis of human studies.

Annals of medicine·2026
Same author

HIV-1 variants in population-based national surveys in sub-Saharan Africa, 2015-2022.

AIDS (London, England)·2026
Same author

Neonatal mortality attributable to preterm births associated with maternal HIV infection in sub-Saharan Africa.

AIDS (London, England)·2026
Same author

Antenatal prediction of small for gestational age at birth based on four birthweight standards using machine learning algorithms.

Frontiers in artificial intelligence·2026

相关实验视频

Updated: Jun 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

使用机器学习算法预测单子妊娠中早产风险.

Qiu-Yan Yu1,2, Ying Lin3, Yu-Run Zhou3

  • 1National Perinatal Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.

Frontiers in big data
|March 18, 2024
PubMed
概括

机器学习模型可以使用妊娠监测数据预测早产风险. 关键预测因素包括产前检查和母亲健康指标,为早期干预提供了潜力.

关键词:
产前护理 产前护理功能选择 功能选择机器学习是机器学习.预测模型 预测模型过早出生 过早出生

更多相关视频

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

相关实验视频

Last Updated: Jun 30, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

科学领域:

  • 产科和妇科 产科和妇科
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 过早分娩 (<37周妊娠期) 是新生儿发病率和死亡率的主要原因.
  • 准确预测早产仍然是一个临床挑战,需要先进的分析方法.

研究的目的:

  • 开发,培训和验证机器学习模型,用于预测单子妊娠中早产.
  • 用各种机器学习算法识别早产的关键预测特征.

主要方法:

  • 在中国 (2014-2016) 的前性队列研究中利用了22603例单独怀孕的数据.
  • 应用算法包括Catboost,随机森林,DNN,SVM和物流回归用于特征选择和预测.
  • 采用5倍交叉验证用于内部模型验证,并使用接收器操作曲线下的面积 (AUC) 评估性能.

主要成果:

  • 在怀孕26周后应用的CatBoost模型显示出最佳性能,AUC为0.70.
  • 发现的关键预测因素包括产前检查次数,阿斯巴酸氨基转移酶水平,交合体底部高度,母体体重,腹周和血压.
  • 最好的模型实现了0.81的精度,0.47的灵敏度和0.83.8的特异性.

结论:

  • 关于怀孕监测数据的机器学习应用对早产预测有希望.
  • 确定了几种可修改的产前预测因素,表明了早期干预策略的潜在目标.
  • 这项研究强调了先进的计算方法在改善产科结果方面的实用性.