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 journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026
Same journal

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026
Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026
Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026
Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026
Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

JMIR medical informatics·2026
查看所有相关文章

相关实验视频

Updated: May 30, 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

可解释机器学习模型用于预测产后抑郁症:回顾性研究

Ren Zhang1,2, Yi Liu3, Zhiwei Zhang2

  • 1Department of Gynecology and Obstetrics, West China Second University Hospital, Sichuan University, Key Laboratory of Birth Defects and Related Diseases of Women and Children (Sichuan University), Ministry of Education, Chengdu, China.

JMIR medical informatics
|January 26, 2025
PubMed
概括
此摘要是机器生成的。

机器学习可以使用产前抑郁症和甲状腺水平等因素准确预测产后抑郁症 (PPD). 这有助于对患PPD高风险的母亲进行早期查.

关键词:
在 PPDPD 的情况下,PPD 是 PPD.在XGBoost上使用.极端的梯度增强了极端的梯度.机器学习是机器学习.产后抑郁症 产后抑郁症预测模型是一个预测模型.有关风险因素的风险因素.

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

7.4K
Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants
06:39

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Published on: June 13, 2021

3.0K

相关实验视频

Last Updated: May 30, 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
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
Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants
06:39

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Published on: June 13, 2021

3.0K

科学领域:

  • 生殖医学 生殖医学
  • 精神病学是一个精神病学.
  • 计算生物学 计算生物学

背景情况:

  • 产后抑郁症 (PPD) 显著影响到母亲和家庭的福祉.
  • 准确和早期预测PPD仍然是一个临床挑战.

研究的目的:

  • 开发和验证用于精确PPD预测的机器学习模型.
  • 确定关键预测因素及其对PPD的临床影响.

主要方法:

  • 收集了来自2055名孕妇的数据.
  • 使用最少绝对收缩和选择操作员 (LASSO) 回归用于可变选.
  • 使用培训和验证队伍开发和验证机器学习模型.

主要成果:

  • 极端梯度增强模型实现了0.849.9的AUC.
  • 发现的关键预测因素包括产前抑郁,胎儿体重降低,TSH升高,TPOAb降低,费里丁升高和母亲年龄较大.
  • 沙普利添加式解释 (SHAP) 用于模型解释.

结论:

  • 为PPD预测开发了一个经过验证的机器学习模型.
  • 鉴定的生理和心理因素为早期PPD风险查提供了洞察力.
  • 强调需要采用全面的PPD查方法.