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

Data Validation01:03

Data Validation

5.4K
Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
5.4K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

801
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
801
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.9K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.9K

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

Disparities in Distress Symptoms Among Cancer Inpatients, Outpatients and Relatives Through Introducing and Evaluating Digital Distress Screening.

Psycho-oncology·2026
Same author

Integrated, Cross-Entity Information on Preventive Measures for Bowel, Breast, and Prostate Cancer: Evaluation Study of the Web Application "Prevent-Take-Up".

JMIR cancer·2025
Same author

CLL to Richter syndrome: Integrating network strategies with experiments elucidating disease drivers and personalized therapies.

Science advances·2025
Same author

Deep Learning Predicts Postoperative Mobility, Activities of Daily Living, and Discharge Destination in Older Adults from Sensor Data.

Sensors (Basel, Switzerland)·2025
Same author

Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.

Journal of medical Internet research·2025
Same author

Robust signalling entropy estimation for biological process characterisation.

Briefings in bioinformatics·2025
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
Same journal

Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.

JMIR medical informatics·2026
Same journal

Prediction of Early Hospital Admission (≤24 Hours) After Stroke Using Machine Learning and Deep Learning: Multicenter Study From China.

JMIR medical informatics·2026
Same journal

Assessing the Feasibility and Acceptability of Implementing a Preclinic Vital Signs Assessment in Primary Care: Cross-Sectional Pilot Study.

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

相关实验视频

Updated: Sep 17, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K

使用人工智能评估临床数据完整性和生成元数据的建议:算法开发和验证.

Caroline Bönisch1,2, Christian Schmidt2, Dorothea Kesztyüs2

  • 1Department of Electrical Engineering and Informatics, University of Applied Sciences Stralsund, Zur Schwedenschanze 15, Stralsund, 18435, Germany, 49 3831 45 6505.

JMIR medical informatics
|June 30, 2025
PubMed
概括
此摘要是机器生成的。

这项研究展示了用于预测医疗数据质量的机器学习模型,提高了基于证据的医学的可靠性. 支持向量机和XGBoost在不同医疗数据集的数据质量分类方面表现强.

关键词:
在这里,我们可以看到AIAIAI.准确度 准确度 准确度 准确度算法算法是一种算法.人工智能的人工智能是人工智能.临床数据 临床数据数据完整性的数据完整性.数据质量数据质量数据质量发展发展发展发展发展.互操作性互操作性互操作性的互操作性文献审查 文献审查机器学习是机器学习.这些都是元数据.模型模型模型模型模型模型质量质量质量质量质量质量.可靠性的可靠性使用利用利用利用利用利用利用利用利用利用验证验证的时间

更多相关视频

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

269
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.7K

相关实验视频

Last Updated: Sep 17, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
07:50

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

16.0K
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

269
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.7K

科学领域:

  • 医疗信息学 医疗信息学
  • 数据科学数据科学数据科学
  • 医疗保健服务研究 医疗服务研究

背景情况:

  • 基于证据的医学依赖于来自研究和现实世界来源的高质量的临床数据.
  • 预测质量算法和机器学习对于确保数据完整性和患者安全至关重要.
  • 可靠的临床数据对于研究可重复性和从实践中获得见解至关重要.

研究的目的:

  • 评估大学医院的初级临床系统中医疗数据质量的变化.
  • 通过基于机器学习的预测质量算法,为研究人员提供有关数据可靠性的见解.
  • 开发和验证用于预测数据质量的模板,并将这些信息集成到元数据中.

主要方法:

  • 一项文献审查确定了现有的自动化质量预测方法.
  • 在医疗数据集成中心 (MeDIC) 的数据集成过程中收集了元数据,包括细粒度和质量指标.
  • 机器学习算法 (逻辑回归,k-NN,天真贝叶斯,决策树,随机森林,XGBoost,SVM) 在心声图,实验室和药物数据上进行训练和评估.

主要成果:

  • 极端梯度增强 (XGB) 实现了84.6%的AUC-ROC,用于回声心脏学数据质量预测.
  • 支持矢量机器 (SVM) 在实验室数据方面表现出优异的性能,AUC-ROC. 89.8%.
  • 对于药物数据来说,SVM也提供了最平衡的性能,产生了65.1%的AUC-ROC.

结论:

  • 提出了一个新的模板,用于预测数据质量,并将其集成到数据集成中心内的元数据中.
  • 开发的模型与传统方法相结合,用于有效的数据检查.
  • 这种方法提高了临床数据的可靠性和实用性,用于研究和临床决策.