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
z Scores and Area Under the Curve01:17

z Scores and Area Under the Curve

10.9K
z scores are the standardized values obtained after converting a normal distribution into a standard normal distribution. A z score is measured in units of the standard deviation. The z score tells you how many standard deviations the value x is above (to the right of) or below (to the left of) the mean, μ. Values of x that are larger than the mean have positive z scores, and values of x that are smaller than the mean have negative z scores. If x equals the mean, then x has a z score of...
10.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
43

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

Effect of immersive virtual reality on fear, anxiety, and pain in pregnant women during vaginal examination in clinical training: a randomized controlled trial.

Scientific reports·2026
Same author

The Effect of the COVID-19 Pandemic on Precocious Puberty.

Galen medical journal·2026
Same author

Genitourinary malignancy among patients presenting with microscopic hematuria in Northwestern Ontario.

Canadian Urological Association journal = Journal de l'Association des urologues du Canada·2026
Same author

The power of machine learning models in predicting gestational diabetes mellitus.

BMC pregnancy and childbirth·2026
Same author

Exploring sexual health challenges in men with type 2 diabetes-related erectile dysfunction: a qualitative study.

BMC public health·2026
Same author

Evaluation of PRVC and SIMV ventilation techniques on hemodynamic metrics and arterial blood gases in ICU patients with multiple trauma: A randomized, triple-blind study.

Journal of critical care medicine (Universitatea de Medicina si Farmacie din Targu-Mures)·2025
Same journal

The associations between maternal disability and perinatal outcomes among Black and/or Hispanic women in PRAMS.

BMC pregnancy and childbirth·2026
Same journal

Pregnancy and related complications in achondroplasia: a scoping review.

BMC pregnancy and childbirth·2026
Same journal

Evaluating progestin-primed and GnRH antagonist ovarian stimulation protocols in PGT-A cycles: implications for clinical practice.

BMC pregnancy and childbirth·2026
Same journal

Does the number of abnormal values in the oral glucose tolerance test impact pregnancy outcomes?

BMC pregnancy and childbirth·2026
Same journal

RT-qPCR detection of SARS-CoV-2 RNA in placentas of women with spontaneous abortion: a retrospective pilot study.

BMC pregnancy and childbirth·2026
Same journal

Reproductive carrier screening among Chinese couples experiencing unexplained recurrent pregnancy loss.

BMC pregnancy and childbirth·2026
查看所有相关文章

相关实验视频

Updated: Jul 10, 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

8.3K

基于机器学习的方法用于预测低出生体重的预测.

Amene Ranjbar1, Farideh Montazeri2, Mohammadsadegh Vahidi Farashah3

  • 1Fertility and Infertility Research Center, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.

BMC pregnancy and childbirth
|November 21, 2023
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测低出生体重 (LBW). 极端梯度提升模型显示出最佳表现,妊娠年龄和之前的LBW历史作为关键预测因素.

关键词:
出生的体重出生时的体重.胎儿的体重 胎儿的体重低出生体重 低出生体重机器学习 机器学习在X梯度提升模型中,X梯度提升模型

更多相关视频

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

7.6K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.4K

相关实验视频

Last Updated: Jul 10, 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

8.3K
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

7.6K
Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

1.4K

科学领域:

  • 围产期健康 围产期健康
  • 医疗信息学医学信息学
  • 预测分析在产科中的预测分析.

背景情况:

  • 低出生体重 (LBW) 是婴儿死亡率和新生儿健康不良结果的重要风险因素.
  • 准确预测LBW对于及时干预和改善新生儿护理至关重要.
  • 这项研究解决了对产妇和新生儿健康有效预测工具的需求.

研究的目的:

  • 开发和评估用于预测低出生体重 (LBW) 的机器学习模型.
  • 为了比较各种统计学习模型在识别LBW时的诊断性能.
  • 为了确定单独怀孕中LBW的关键预测因素.

主要方法:

  • 利用了来自伊朗母亲和新生儿网络 (IMaN Net) 数据库的数据 (2020年1月 - 2022年1月).
  • 包括单个怀孕>24周妊娠;不包括多胎怀孕和胎儿异常.
  • 评估了8种机器学习模型,包括深度学习,随机森林和极端梯度提升,使用AUROC,准确性,精度,回忆和F1分数进行性能评估.

主要成果:

  • 总共分析了8853例分娩,低出生体重 (LBW) 的发生率为14.5% (1280例).
  • 深度学习 (AUROC:0.86),随机森林 (AUROC:0.79) 和极端梯度提升 (AUROC:0.79) 展示了卓越的性能.
  • 极端梯度提升模型实现了最高的诊断性能,准确度为0.79,精度为0.87,回忆率为0.69,F1得分为0.77.

结论:

  • 极端梯度提升模型表现出对低出生体重 (LBW) 的强大预测能力.
  • 怀孕年龄和之前的LBW史被确定为关键预测因素.
  • 需要进一步的研究来确定最佳的机器学习模型来预测LBW.