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

Survival Tree01:19

Survival Tree

51
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
51

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

Screen Time and Social Development Through Play in Early Childhood: A Cross-Sectional Study.

Children (Basel, Switzerland)·2026
Same author

On the usage of artificial intelligence for identifying main attributes and predicting neonatal sepsis.

Scientific reports·2026
Same author

Chronic phase of Chikungunya: understanding the impact of joint pain using data science and artificial intelligence.

Tropical medicine and health·2026
Same author

Relationship between social participation and stigma in people affected by leprosy: A cross-sectional study in northeastern Brazil.

Belitung nursing journal·2026
Same author

Machine learning for preventing stillbirths: is it possible to transform data into life-saving insights?

BMC pregnancy and childbirth·2025
Same author

On the usage of artificial intelligence in leprosy care: A systematic literature review.

PLoS computational biology·2025

相关实验视频

Updated: May 25, 2025

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

7.4K

利用基于树的机器学习模型来预测低出生体重病例.

Flavio Leandro de Morais1, Elisson da Silva Rocha1, Gabriel Masson1

  • 1Programa de Pós-Graduação em Engenharia da Computação (PPGEC), Universidade de Pernambuco (UPE), Recife, Pernambuco, Brazil.

BMC pregnancy and childbirth
|February 26, 2025
PubMed
概括

机器学习模型可以预测新生儿的低出生体重 (LBW). 删除重复数据和选择关键属性提高了模型的准确性,突出了社会人口统计学因素和妊娠史作为关键预测因素.

关键词:
低出生体重 低出生体重机器学习是机器学习.预测 预测 预测产前护理 产前护理

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

6.6K
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

相关实验视频

Last Updated: May 25, 2025

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

7.4K
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

6.6K
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

科学领域:

  • 计算医学是一种计算医学.
  • 新生儿健康研究新生儿健康研究
  • 机器学习在医疗保健中的应用

背景情况:

  • 低出生体重 (LBW) 影响全球超过2000万名新生儿.
  • 机器学习 (ML) 为早期LBW预测和干预提供了潜力.
  • 预测模型可以指导怀孕期间治疗调整和饮食建议.

研究的目的:

  • 评估机器学习模型来预测孕妇LBW风险.
  • 识别与LBW相关的新生儿不良后果的高风险孕妇.

主要方法:

  • 在四个不同的场景中进行数据分析和属性选择.
  • 使用交叉验证和超参数优化验证五个机器学习模型的验证.
  • 使用七个指标和统计分析来评估LBW预测效率的性能评估.

主要成果:

  • 模型性能在不同场景中各不相同;删除重复数据改善了回忆 (0.83) 和F1得分 (0.64).
  • 统计分析表明模型性能存在显著差异 (p < 0.05).
  • 属性重要性分析确定了关键的预测因素.

结论:

  • 删除重复数据和仔细的属性选择提高了ML模型的性能,用于LBW预测.
  • 社会人口特征和妊娠史是模型培训中最有影响力的因素.
  • 优化的ML模型可以帮助识别有风险的怀孕,以便及时进行干预.