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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

27
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...
27
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
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K

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

Designing Scalable Mechano-Virucidal Nanostructured Acrylic Surfaces for Enhanced Viral Inactivation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Developing count regression techniques for predicting the number of new type 2 diabetes cases in Saudi Arabia.

PloS one·2026
Same author

Predicting early and late neonatal mortality using machine learning models in Oman.

BMC public health·2025
Same author

Enhancing node influence prediction in large networks via multi-Level knowledge distillation.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

A hybrid approach to enhance HbA1c prediction accuracy while minimizing the number of associated predictors: A case-control study in Saudi Arabia.

PloS one·2025
Same author

Modeling the number of new cases of childhood type 1 diabetes using Poisson regression and machine learning methods; a case study in Saudi Arabia.

PloS one·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: Jun 4, 2025

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

7.0K

使用机器学习模型预测母亲的风险水平.

Sulaiman Salim Al Mashrafi1,2, Laleh Tafakori3, Mali Abdollahian3

  • 1School of Science, RMIT University, Melbourne, Victoria, Australia. S3912607@student.rmit.edu.au.

BMC pregnancy and childbirth
|December 18, 2024
PubMed
概括
此摘要是机器生成的。

机器学习模型可以预测母亲的健康风险. 随机森林模型在识别高风险怀孕方面表现最好,有助于早期干预以减少孕产妇死亡率.

关键词:
机器学习 机器学习孕产妇死亡率的比率孕产妇死亡风险 孕产妇死亡风险阿曼阿曼阿曼阿曼阿曼阿曼阿曼阿曼预测 预测 预测主要组成部分分析 (PCA)

更多相关视频

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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 4, 2025

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

7.0K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K
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:

  • 公共卫生 公共卫生
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 孕产妇发病率和死亡率是关键的全球卫生问题,降低孕产妇死亡率 (MMR) 是可持续发展目标 (SDG) 的一个关键目标.
  • 准确预测孕产妇健康风险对于有针对性的干预措施至关重要,但仍然具有挑战性.
  • 机器学习 (ML) 提供了一种有前途的方法,用于开发准确的预测模型,以预测母亲的健康结果.

研究的目的:

  • 探索各种ML算法的有效性,以预测孕产妇的风险水平.
  • 利用全国性的阿曼孕产妇死亡数据集用于基于ML的风险预测.
  • 为数据驱动的战略奠定基础,以减轻孕产妇死亡率.

主要方法:

  • 利用了阿曼402例孕产妇死亡数据集 (1991-2023年).
  • 应用并比较了十个ML算法,包括随机森林 (RF),有和没有主要组件分析 (PCA).
  • 使用准确度,灵敏度,精度和F1分数等指标评估模型性能.

主要成果:

  • 随机森林 (RF) 模型在应用PCA后,在预测孕产妇风险水平方面表现优异.
  • 优化的射频模型在风险分类方面实现了75.2%的准确性,85.7%的精度和73%的F1得分.
  • 这表明ML的潜力,特别是RF,在准确识别高风险的孕产妇病例.

结论:

  • 机器学习模型成功地应用到使用阿曼数据来预测母亲的风险水平.
  • 随机森林算法被证明是这个分类任务中最有效的.
  • 准确的孕产妇风险预测可以显著帮助医疗保健提供者制定及时的干预计划,以减少孕产妇死亡率.