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

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

2.1K
Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
2.1K
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

487
For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
487
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

2.3K
Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
2.3K
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

10.6K
Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
10.6K
SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

4.2K
SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
4.2K

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

Patient Perspectives on an Autonomous Wheelchair Transport Pilot in a Tertiary Medical Center: A Cross-Sectional Survey.

Journal of primary care & community health·2026
Same author

Factors Associated With Alcohol Intake in Patients With Newly Diagnosed Breast Cancer.

Cancer medicine·2026
Same author

Trends and Sex-Specific Rates of Obesity and Severe Obesity in US Hospitalized Adults, 2012-2021.

Journal of general internal medicine·2026
Same author

More Than Stress Hyperglycemia: Acute Pancreatitis as a Prelude to Diabetes.

Diabetes care·2026
Same author

Genotype epigenome phenotype integration reveals peripheral immune contributions to type I bipolar disorder.

Nature communications·2026
Same author

Artificial intelligence for predicting hospital admissions from the emergency department: a prospective, quasi-experimental study.

Nature communications·2026

相关实验视频

Updated: May 15, 2025

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

3.5K

使用电子健康记录和自我报告数据识别2型糖尿病的算法.

Ben T Varghese1,2, Marlene E Girardo3, Ruchi Gupta4

  • 1Division of Hospital Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Metabolic syndrome and related disorders
|April 7, 2025
PubMed
概括
此摘要是机器生成的。

结合电子健康记录和自我报告数据的算法准确地识别了2型糖尿病 (T2D). 这种方法改善了研究队列中的T2D分类,提高了研究数据的可靠性.

关键词:
算法算法是一种算法.生物银行生物银行糖尿病 糖尿病患者 糖尿病患者电子健康记录 电子健康记录自我报告 自我报告

更多相关视频

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases
06:50

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases

Published on: May 29, 2020

2.4K
Electrochemiluminescence Assays for Human Islet Autoantibodies
09:15

Electrochemiluminescence Assays for Human Islet Autoantibodies

Published on: March 23, 2018

15.4K

相关实验视频

Last Updated: May 15, 2025

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

3.5K
A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases
06:50

A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases

Published on: May 29, 2020

2.4K
Electrochemiluminescence Assays for Human Islet Autoantibodies
09:15

Electrochemiluminescence Assays for Human Islet Autoantibodies

Published on: March 23, 2018

15.4K

科学领域:

  • 生物医学信息学 生物医学信息学
  • 临床研究 临床研究

背景情况:

  • 准确识别患有2型糖尿病 (T2D) 的参与者对于临床研究至关重要.
  • 仅依靠电子健康记录 (EHR) 或自我报告的数据在T2D分类准确性方面存在局限性.

研究的目的:

  • 开发和验证一个算法,整合EHR和自我报告数据,以准确识别患有和没有T2D的个人.
  • 为了提高T2D病例确定在大型生物库队列中的准确性.

主要方法:

  • 利用了梅奥诊所生物库的数据,包括基线问卷和电子病历数据 (ICD代码,HbA1c,葡萄糖,药物).
  • 开发了一种算法,将参与者分为T2D,没有T2D",只有自我报告的T2D"和"只有自我报告的没有T2D"类别.
  • 使用手动图表审查作为黄金标准验证了算法的性能,计算正预测值 (PPV) 和负预测值 (NPV).

主要成果:

  • 算法对57,000名参与者进行了分类:6,238人患有T2D,38,883人没有T2D,757人"只有自我报告的T2D",9759人"只有自我报告的没有T2D".
  • 实现了高性能指标:PPV为96.0%,NPV为100%,整体准确率为99.5%.
  • 在各分类组的年龄和性别分布中观察到显著的差异.

结论:

  • 开发的算法证明了高准确性和可靠性,用于识别T2D和没有T2D的个人,使用组合的EHR和自我报告数据.
  • 这种经过验证的算法为研究环境中的T2D确定提供了一个强大的工具,可能适用于与链接EHR数据的其他队列.