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

GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

73
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
73
Data Validation01:03

Data Validation

5.1K
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...
5.1K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
27
Data Collection by Observations01:08

Data Collection by Observations

12.0K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
12.0K
Data Reporting and Recording01:24

Data Reporting and Recording

4.7K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
4.7K
Levels of Use of a GIS01:29

Levels of Use of a GIS

54
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
54

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

Improving the Quality and Utility of Electronic Health Record Data through Ontologies.

Standards (Basel, Switzerland)·2023
Same author

Supporting SNOMED CT postcoordination with knowledge graph embeddings.

Journal of biomedical informatics·2023
Same author

Towards a Semantic Data Harmonization Federated Infrastructure.

Studies in health technology and informatics·2021
Same author

Analysis of readability and structural accuracy in SNOMED CT.

BMC medical informatics and decision making·2020
Same author

Clinical Text Mining on FHIR.

Studies in health technology and informatics·2019
Same author

Towards the semantic enrichment of Computer Interpretable Guidelines: a method for the identification of relevant ontological terms.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2019
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
查看所有相关文章

相关实验视频

Updated: Jul 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K

一个基于知识图的数据协调框架,用于二次数据的再利用.

Francisco Abad-Navarro1, Catalina Martínez-Costa1

  • 1Departamento de Informática y Sistemas, Universidad de Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, 30100, Murcia, Spain.

Computer methods and programs in biomedicine
|November 19, 2023
PubMed
概括
此摘要是机器生成的。

本研究引入了一个语义驱动的框架,用于协调异质的医疗保健数据,使跨机构的数据共享和分析能够用于机器学习应用.

关键词:
知识图是知识图.存在学 (Ontologies) 是一种存在学.这就是SNOMED CT.语义互操作性的语义互操作性语义查询 语义查询是一个语义查询.

更多相关视频

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

236
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.2K

相关实验视频

Last Updated: Jul 10, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
07:35

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

Published on: October 13, 2023

1.7K
Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

236
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

2.2K

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
  • 本体学 工程学 工程学

背景情况:

  • 临床护理系统产生大量的异质数据.
  • 数据协调对于整合和分析这些有价值的信息至关重要.

研究的目的:

  • 提出医疗保健数据的语义驱动的协调框架.
  • 实现跨机构数据的有意义的共享和整合.
  • 为了促进先进的数据分析和利用.

主要方法:

  • 开发了一个基于本体学的常用数据模型 (SCDM).
  • 实现了一个数据转换管道和一个语义查询系统.
  • 利用基于本体学的基础架构和图形数据库进行集成.

主要成果:

  • 在Precise4Q项目中,成功地整合了来自多个欧洲机构的异质数据集.
  • 使数据科学家能够通过语义查询系统探索集成的数据.
  • 促进了统一数据的使用,用于构建机器学习模型.

结论:

  • 语义共同数据模型 (SCDM) 和RDF使异质医疗数据的语义整合成为可能.
  • 该框架支持用于研究和临床应用的先进数据利用.
  • 在欧洲H2020 Precise4Q项目中证明了成功的应用.