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

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

779
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:
779
Urinary Tract Infection II: Pathophysiology01:25

Urinary Tract Infection II: Pathophysiology

1.5K
The pathophysiology of urinary tract infections (UTIs) encompasses several progressive stages, beginning with bacterial colonization and culminating in potential systemic complications if untreated. UTIs are primarily initiated by bacteria, such as Escherichia coli, which often originate from the gastrointestinal tract and migrate to the urinary system through the periurethral area. This migration can occur via several routes, including improper hygiene practices, sexual activity, or...
1.5K
Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care01:30

Urinary Tract Infection III: Diagnostic Studies and Interprofessional Care

434
A healthcare provider can diagnose a urinary tract infection (UTI) through several methods:Medical History and Symptoms: The provider will take a detailed medical history and ask about symptoms such as frequent urination, burning sensation during urination, and lower abdominal pain.Urinalysis: A clean-catch urine sample is collected in a sterile container and tested for the presence of bacteria, white blood cells (leukocytes), nitrites, blood, and protein. The presence of leukocytes and...
434
Urinary Tract Infection IV: Nursing Management01:17

Urinary Tract Infection IV: Nursing Management

683
In managing urinary tract infections (UTIs) in nursing, a comprehensive assessment is essential. Begin by gathering subjective data, such as the patient’s complaints of dysuria (painful urination), urinary frequency, urgency, suprapubic pain, and any lower abdominal discomfort. This information can be complemented by questions regarding previous UTIs, sexual activity, and personal hygiene practices, which can provide insight into risk factors. Objective assessment should focus on signs...
683
Acute Pyelonephritis II: Diagnostic Studies and Management01:28

Acute Pyelonephritis II: Diagnostic Studies and Management

842
Introduction:For diagnosing acute pyelonephritis, a comprehensive patient history is collected to identify symptoms such as dysuria, frequent or urgent urination, flank pain, or costovertebral angle (CVA) tenderness that may suggest a kidney infection.Physical ExaminationDuring the physical examination, CVA tenderness is assessed. This involves gentle percussion over the costovertebral angle, where tenderness often indicates a kidney infection.Diagnostic TestsUrinalysis: Used to identify white...
842
Urinary Tract Calculi V: Nursing Management01:28

Urinary Tract Calculi V: Nursing Management

494
AssessmentSubjective Data: Obtain a detailed health history, including any recent or chronic urinary tract infections, periods of immobilization, previous episodes of renal calculi, and medical conditions such as gout, benign prostatic hyperplasia, or hyperparathyroidism. Review the medication history for drugs that may influence stone formation, including allopurinol, analgesics, loop diuretics, or thiazide diuretics. Document the use of long-term indwelling catheters and any past surgical...
494

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

Which features best differentiate Sjögren's-related from non-Sjögren's-related dry eye disease?

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026
Same author

Ophthalmic involvement in VEXAS syndrome and its influence on mortality: insights from the international AIDA network registry.

Frontiers in immunology·2026
Same author

Corneal nerve alterations in migraine: a systematic review of in vivo confocal microscopy and esthesiometry findings.

Contact lens & anterior eye : the journal of the British Contact Lens Association·2026
Same author

Toxoplasmosis meets the World Health Organization criteria for a neglected tropical disease.

PLoS neglected tropical diseases·2026
Same author

Vogt-Koyanagi-Harada disease frequency around the globe: a systematic review and meta-analysis.

Eye (London, England)·2026
Same author

Drug therapies and immune pathways in ocular manifestations of inflammatory skin diseases.

Expert review of clinical immunology·2026

相关实验视频

Updated: May 2, 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

8.3K

使用基线临床特征预测尿道炎的复发过程的机器学习

William Rojas-Carabali1,2,3, Carlos Cifuentes-González1,3, Anna Utami4

  • 1Programme for Ocular Inflammation & Infection Translational Research, Department of Ophthalmology, National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Singapore, Singapore.

Investigative ophthalmology & visual science
|August 27, 2025
PubMed
概括

机器学习模型可以预测高特异性低膜炎复发风险,帮助临床决策. 然而,由于敏感性有限,预测这种复杂疾病的罕见事件仍然具有挑战性.

更多相关视频

Experimental Autoimmune Uveitis: An Intraocular Inflammatory Mouse Model
07:40

Experimental Autoimmune Uveitis: An Intraocular Inflammatory Mouse Model

Published on: January 12, 2022

5.1K
Primed Mycobacterial Uveitis PMU as a Model for Post-Infectious Uveitis
10:33

Primed Mycobacterial Uveitis PMU as a Model for Post-Infectious Uveitis

Published on: December 17, 2021

2.8K

相关实验视频

Last Updated: May 2, 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

8.3K
Experimental Autoimmune Uveitis: An Intraocular Inflammatory Mouse Model
07:40

Experimental Autoimmune Uveitis: An Intraocular Inflammatory Mouse Model

Published on: January 12, 2022

5.1K
Primed Mycobacterial Uveitis PMU as a Model for Post-Infectious Uveitis
10:33

Primed Mycobacterial Uveitis PMU as a Model for Post-Infectious Uveitis

Published on: December 17, 2021

2.8K

科学领域:

  • 眼科 眼科
  • 人工智能
  • 医疗信息学

背景情况:

  • 阴道炎是一种复杂的眼内炎症.
  • 预测脑膜炎复发对于有效的患者管理和风险分层至关重要.
  • 目前用于预测复发的方法有局限性.

研究的目的:

  • 开发和评估用于预测复发性脑膜炎风险的机器学习 (ML) 模型.
  • 使用基线临床特征进行风险分层.
  • 为了指导临床决策在尿膜炎的管理.

主要方法:

  • 来自眼部自身免疫系统性炎症传染病研究的966名患者的回顾性分析.
  • 在基线数据上培训三个ML分类器 (随机森林,极端梯度增强,RBF-SVC).
  • 通过双变量分析和使用交叉验证的网格搜索优化特征选择.

主要成果:

  • 随机森林模型实现了最高准确度 (0.77) 具有高特异性 (0.93),但适度灵敏度 (0.44).
  • 极端梯度提升和RBF-SVC显示了可比的准确性.
  • 已发现的关键预测因素包括玻璃体雾,反细胞和非传染性病因.

结论:

  • ML模型,特别是随机森林,在识别患有膜炎复发风险较低的患者方面表现有前途.
  • 高特异性表明可靠的低风险个体的识别.
  • 在异质患者群体中预测罕见事件的持续挑战.