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

Classification of Illness01:17

Classification of Illness

8.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.6K
Binge Eating Disorders01:23

Binge Eating Disorders

419
Binge eating disorder is a significant mental health condition characterized by recurrent episodes of excessive food consumption within a short period, accompanied by a perceived loss of control over eating behavior. Unlike occasional overeating, binge eating disorder is marked by distressing emotions such as guilt, shame, and anxiety following binge episodes. The disorder affects individuals across different ages and backgrounds, with profound implications for physical and psychological...
419
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

488
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
488

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

An Evaluation of Pretrained Generative Models for Augmenting Small Health Data: Comparative Modeling Study.

Journal of medical Internet research·2026
Same author

Transfer Learning and Machine Learning for Training Five-Year Survival Prognostic Models in Early Breast Cancer: Development and Validation Study.

Journal of medical Internet research·2026
Same author

FREEDcan: an integrated early intervention for eating disorders care model for community and primary care settings in Canada.

Translational behavioral medicine·2026
Same author

Research on Eating and Adolescent Lifestyle (REAL) 2.0: 15-year follow-up study of eating disorders and weight-related trajectories, mental health and substance use health from early adolescence to early adulthood-a Canadian cohort profile.

BMJ open·2026
Same author

Should we synthesize more than we need: impact of synthetic data generation for high-dimensional cross-sectional medical data.

Journal of the American Medical Informatics Association : JAMIA·2025
Same author

Magnitude and Impact of Hallucinations in Tabular Synthetic Health Data on Prognostic Machine Learning Models: Validation Study.

Journal of medical Internet research·2025

相关实验视频

Updated: Jan 15, 2026

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.3K

应用机器学习来预测患有饮食障碍的年轻人复杂的临床过程

Stephanie Ryall1,2, Abigail Bradley1, Khaled El Emam1,3

  • 1Children's Hospital of Eastern Ontario Research Institute, Ottawa, Ontario, Canada.

The International journal of eating disorders
|October 13, 2025
PubMed
概括

监督机器学习模型在预测青少年复杂饮食障碍轨迹方面显著优于逻辑回归. 结合摄入和排出数据,提高了识别风险人群的预测准确性.

关键词:
临床课程 临床课程饮食障碍 饮食障碍 饮食障碍机器学习是机器学习.预测 预测 预测 预测随机的森林随机的森林

更多相关视频

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

8.6K
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

7.5K

相关实验视频

Last Updated: Jan 15, 2026

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

4.3K
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

8.6K
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

7.5K

科学领域:

  • 儿童和青少年精神病学 儿童和青少年精神病学
  • 在医疗保健中的数据科学.
  • 饮食障碍研究 饮食障碍研究

背景情况:

  • 识别患有饮食障碍 (ED) 的年轻人有复杂临床过程的风险,对于及时干预至关重要.
  • 传统的统计方法可能在从多方面的临床数据中预测复杂疾病轨迹方面存在局限性.

研究的目的:

  • 将监督机器学习 (ML) 模型的预测性能与后勤回归进行比较.
  • 通过使用他们第一次治疗事件的临床特征来识别患有ED的年轻人有复杂临床过程的风险.

主要方法:

  • 利用了327名因ED治疗的青少年的临床数据.
  • 定义复杂的临床过程通过再接收或非逐步下降的治疗轨迹.
  • 使用嵌套交叉验证对34个摄入和排放变量进行了训练七个ML模型和后勤回归.

主要成果:

  • 随机森林模型,使用摄入和排放数据,实现了最高的性能 (AUC=0.723,Brier=0.176),超过了后勤回归.
  • 仅使用摄入数据的模型显示预测歧视差 (AUC < 0.6).
  • 包括放电数据在所有ML算法中提高了性能;重量变化是最重要的预测因素.

结论:

  • 与传统方法相比,监督的ML模型为ED疾病过程的结果提供了更好的预测性能.
  • 这些发现支持使用ML来分析复杂的生物心理社会数据,用于ED治疗中的精密医学.
  • 进一步应用ML可以提高对ED病因和疾病轨迹的理解.