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

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
Cerebral Hemispheres01:05

Cerebral Hemispheres

The human brain, a complex organ, is functionally divided into two cerebral hemispheres—left and right. These hemispheres are interconnected by a structure of paramount importance, the corpus callosum. This substantial bundle of neural fibers is not just a bridge between the hemispheres but a crucial element for the brain's comprehensive functioning. It enables efficient communication between the two hemispheres, allowing each side of the brain to control and receive sensory and motor...
Lateralization01:28

Lateralization

Brain lateralization refers to the division of mental processes and functions between the two hemispheres of the brain, a phenomenon that optimizes neural efficiency and underpins complex abilities in humans. This specialization allows each hemisphere to perform tasks where it has a comparative advantage, facilitating more refined cognitive capabilities across different domains.
Language and Cognition01:27

Language and Cognition

Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
Learning Disabilities01:25

Learning Disabilities

Learning disabilities are cognitive disorders caused by neurological impairments that affect cognitive functions like language and reading, without indicating overall intellectual or developmental challenges. These disabilities differ from global intellectual or developmental disabilities as they are limited to distinct cognitive functions. Common learning disabilities include dysgraphia, dyslexia, and dyscalculia, each of which impacts unique aspects of learning.
Dyslexia
Dyslexia is a...

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

Exploratory Evaluation of Hydrogen-Rich Water Therapy for Keloid Management: A Double-Blinded Randomized Pilot Trial.

Aesthetic plastic surgery·2026
Same author

A Nomogram with the Keloid Activity Assessment Scale for Predicting the Recurrence of Chest Keloid after Surgery and Radiotherapy.

Aesthetic plastic surgery·2022
Same author

Correction to: Clinical Observation of Subepidermal Vascular Network Flaps in Keloid Patients.

Aesthetic plastic surgery·2022
Same author

Histology and Vascular Architecture Study of Keloid Tissue to Outline the Possible Terminology of Keloid Skin Flaps.

Aesthetic plastic surgery·2022
Same author

Clinical Observation of Subepidermal Vascular Network Flaps in Keloid Patients.

Aesthetic plastic surgery·2022
Same author

Large chest keloids treatment with expanded parasternal intercostal perforator flap.

BMC surgery·2021

相关实验视频

Updated: Jun 18, 2026

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

基于机器学习的基质回归预测模型的比较.

Yan Hao1, Mengjie Shan1, Hao Liu1

  • 1Department of Plastic and Cosmetic Surgery, Peking Union Medical College Hospital, Beijing, China.

Journal of cosmetic dermatology
|February 7, 2025
PubMed
概括

机器学习模型可以预测 keloid 复发. 后勤回归模型基于ROC曲线下的面积 (AUC) 显示出最佳的预后性能,这表明它在预测 keloid 复发方面的有效性.

关键词:
凯洛伊德 (Keloid) 是一种类型的物质.机器学习是机器学习.预测模型 预测模型复发性 复发性 复发性有关风险因素的风险因素.

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
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.0K

相关实验视频

Last Updated: Jun 18, 2026

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
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
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.0K

科学领域:

  • 皮肤病学 皮肤病学
  • 医疗信息学 医疗信息学
  • 生物统计学 生物统计学

背景情况:

  • 治疗后状体的复发是一个重大的临床挑战.
  • 准确预测胆固醇的复发对于优化患者管理和改善结果至关重要.

研究的目的:

  • 开发和比较三种机器学习模型,用于预测 keloid 复发.
  • 为了确定影响 keloid 复发的关键因素.
  • 评估后勤回归,决策树和随机森林模型的预测性能.

主要方法:

  • 包括301名接受手术和放射治疗的化体患者.
  • 模型对70%的数据进行了训练,对30%的数据进行了验证.
  • 使用准确度,灵敏度,特异性,精度,回忆,卡帕系数和AUC来评估性能.

主要成果:

  • 机器学习模型确定了KAAS,平均动脉压,术后并发症和炎症细胞比例作为关键预测因素.
  • 决策树模型实现了最高的准确性和精度.
  • 后勤回归模型在AUC方面表现最好.

结论:

  • 成功建立了三种用于预测 keloid 复发的机器学习模型.
  • KAAS,血压,术后并发症和炎症细胞比例是重要的因素.
  • 后勤回归模型基于AUC提供了最有利的预后性能.