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

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.2K
The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters...
1.2K
Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

567
The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
567
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

2.7K
Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
2.7K
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

2.6K
A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains...
2.6K
Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

277
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
277
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

133
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
133

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

SCOT+: a comprehensive software suite for single-cell alignment using optimal transport.

Bioinformatics advances·2026
Same author

Diffusion-based Representation Integration for Foundation Models Improves Spatial Transcriptomics Analysis.

bioRxiv : the preprint server for biology·2025
Same author

Inferring the regulation dynamics of oscillatory networks from scRNA-seq data.

bioRxiv : the preprint server for biology·2025
Same author

A deep learning model to predict glioma recurrence using integrated genomic and clinical data.

Communications medicine·2025
Same author

Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.

PLoS computational biology·2025
Same author

Single-cell genomics of the mouse olfactory cortex reveals contrasts with neocortex and ancestral signatures of cell type evolution.

Nature neuroscience·2025
Same journal

Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.

JMIR medical informatics·2026
Same journal

Graph Network Feature Space Fusion for Predicting Irregularly Sampled Medical Time-Series Data: Deep Learning Model Development and Validation Study.

JMIR medical informatics·2026
Same journal

Intrasystem Repeatability of S-Detect for Breast Ultrasound Classification With Identical Static Images: Single-Center Retrospective Repeatability Study.

JMIR medical informatics·2026
Same journal

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

JMIR medical informatics·2026
Same journal

IdeaDistiller-AI Support for Idea Synthesis in Concept Mapping: Algorithm Development and Validation Study.

JMIR medical informatics·2026
Same journal

Pregnancy-Related Clinical Codes in Unlikely Populations in Primary Care.

JMIR medical informatics·2026
查看所有相关文章

相关实验视频

Updated: Jun 23, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K

基于检索的诊断决策支持:混合方法研究研究

Tassallah Abdullahi1, Laura Mercurio2, Ritambhara Singh1,3

  • 1Department of Computer Science, Brown University, Providence, RI, United States.

JMIR medical informatics
|June 19, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了CliniqIR,这是一个信息检索 (IR) 框架,可以增强诊断决策支持,特别是对于数据有限的罕见疾病. 开发的整体模型通过结合检索和监督方法来实现最先进的诊断预测.

关键词:
欧洲人权理事会 欧洲人权理事会在RAG RAG的基础上.临床决策支持 临床决策支持数据的稀疏性数据的稀疏性.电子健康记录 电子健康记录电子健康记录是电子健康记录.组合学习组合学习信息检索 信息检索机器学习是机器学习.自然语言处理自然语言处理.罕见的疾病 罕见的疾病获取增强代的恢复提取增强学习的学习.

更多相关视频

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

3.8K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

768

相关实验视频

Last Updated: Jun 23, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.5K
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

3.8K
Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management
05:35

Author Spotlight: Developing a Point-of-Care Hemoglobin Estimation Method for Anemia Management

Published on: January 19, 2024

768

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 临床决策支持系统 临床决策支持系统

背景情况:

  • 诊断错误显著影响患者的安全和死亡率.
  • 机器学习 (ML) 有望通过使用电子健康记录来提高诊断准确性.
  • 现有的ML模型往往忽略了有限的可用培训数据的疾病,阻碍了广泛的诊断支持.

研究的目的:

  • 开发一个基于信息检索 (IR) 的框架,CliniqIR,以解决诊断中的数据稀疏性.
  • 促进更广泛的诊断决策支持,特别是对于罕见或代表性不足的疾病.
  • 创建一个可以适应各种IR框架的系统,包括密集和稀疏的检索方法.

主要方法:

  • 使用临床文本,UMLS Metathesaurus和PubMed摘要开发了CliniqIR,用于广泛的诊断分类.
  • 采用密集和稀疏检索技术实施了CliniqIR.
  • 将CliniqIR与ClinicalBERT (变压器的临床双向编码器表示) 在监督和零射击环境中进行比较.
  • 创建了一个整体框架,将监督的ClinicalBERT和CliniqIR结合起来,以实现卓越的性能.

主要成果:

  • 在没有训练数据的DC3数据集上,CliniqIR在前3个预测中确定了正确的诊断.
  • 在MIMIC-III数据集上,CliniqIR的表现优于ClinicalBERT的表现,诊断的培训样本少于5个 (平均值为5个培训样本). 的MRR差异为0.10).
  • 在零射击评估中,CliniqIR超过了预训练过的变压器模型,平均互惠等级 (MRR) 至少为0.10.
  • 与单个组件相比,整体框架显示了更高的诊断预测准确性.

结论:

  • 信息检索 (IR) 对于利用非结构化数据来诊断罕见疾病至关重要.
  • 整体框架有效地结合了基于监督和检索的模型,提供了全面的诊断能力.
  • 这种方法扩大了诊断决策支持系统的范围,包括不经常遇到的情况.