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

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

749
Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
Troponins
Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
749
Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

516
Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
516

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

Dynamic Risk Trajectories for Sudden Cardiac Arrest: The Role of Recurrent Cardiovascular Events.

Journal of the American Heart Association·2026
Same author

Variation in Telehealth Use in a National Home Test-to-Treat Program for Acute Respiratory Infections.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association·2026
Same author

Variation in Telehealth Use in a National Home Test-to-Treat Program for Acute Respiratory Infections.

medRxiv : the preprint server for health sciences·2026
Same author

Validation and Early Application of the ProVent Score in a Contemporary ICU Cohort.

Journal of intensive care medicine·2026
Same author

Leveraging routine clinical data for dementia risk prediction using machine learning.

Journal of Alzheimer's disease : JAD·2026
Same author

Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.

Journal of the International Neuropsychological Society : JINS·2026
Same journal

Evidence for progressive neurodegeneration in iatrogenic cerebral amyloid angiopathy.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Human brain connectome profiles mediate the relationship between pathology burden and clinical phenotypes in Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Kat5 cKO mouse replicates biological domain signatures associated with Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Evaluation of CSF and plasma tau species as fluid surrogate candidates for tau PET in prodromal to moderate Alzheimer's disease.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Associations of self-reported obstructive sleep apnea with cognition and dementia risk in cognitively unimpaired middle-aged adults.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
Same journal

Inflammation profiles in Alzheimer's disease relate to cognition and neurodegeneration.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2026
查看所有相关文章

相关实验视频

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

37.1K

生物标志物 生物标志物

Zehao Ye1, Amelia Zai1, Biqi Wang1

  • 1University of Massachusetts Medical School, Worcester, MA, USA.

Alzheimer's & dementia : the journal of the Alzheimer's Association
|December 26, 2025
PubMed
概括
此摘要是机器生成的。

使用电子健康记录 (EHR) 的机器学习模型可以预测痴呆风险. XGBoost模型显示了最高的预测性能,有助于早期诊断和干预.

更多相关视频

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

702

相关实验视频

Last Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies

Published on: January 28, 2014

37.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

702

科学领域:

  • 计算医学是一种计算医学.
  • 老年医学 老年医学
  • 数据科学数据科学数据科学

背景情况:

  • 早期痴呆症诊断对于及时干预和改善患者结果至关重要.
  • 电子健康记录 (EHR) 为识别痴呆症风险因素提供了有价值的纵向数据.
  • 这项研究利用机器学习和EHR数据来预测痴呆风险.

研究的目的:

  • 开发和评估用于预测痴呆风险的机器学习模型.
  • 利用来自大型医院系统的纵向EHR数据来预测痴呆症风险.
  • 评估并发症和实验室测试结果对痴呆风险预测的影响.

主要方法:

  • 使用了UMass纪念医疗中心 (2017-2024) 的EHR数据.
  • 使用ICD-10代码识别的痴呆病例,不包括65岁以下的患者.
  • 采用后勤回归,随机森林和XGBoost模型,使用21个特征,包括并发症和实验室测试,通过5倍交叉验证进行评估.

主要成果:

  • 该研究包括30162名参与者 (平均年龄80±12岁,69.7%是女性).
  • 仅使用并发症,XGBoost模型实现了最高的预测性能,AUC为0.82.
  • 整合实验室测试信息提高了性能,XGBoost的AUC达到0.83.

结论:

  • 开发了能够使用EHR数据预测痴呆风险的机器学习模型.
  • 这些发现强调了电子健康记录在早期痴呆症检测方面的潜力.
  • 建议在不同的医疗保健系统中进一步验证.