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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/clinical-data-models-and-data-quality-assessments
课程评论：没有评论
课程名称：临床数据模型与数据质量评估 课程概述：本课程旨在教授临床数据模型和通用数据模型的概念。完成课程后，学习者将能够使用实体关系图（ERD）解读和评估数据模型设计，区分不同的数据模型，并阐明每种模型如何支持临床护理和数据科学。此外，学习者还将能够在Google BigQuery中创建SQL语句，以查询MIMIC3临床数据模型和OMOP通用数据模型。 课程大纲： 1. **导论：临床数据模型与通用数据模型** - 本周介绍临床数据模型，并解释在国家和国际数据网络中使用通用数据模型的必要性和用法。同时，涵盖实体关系图（ERD）的特点，以描述数据模型的关键技术特征。 2. **工具：查询临床数据模型** - 深入研究临床数据模型的技术特性，以MIMIC3为例，研究OMOP的通用数据模型。 3. **技术：提取-转换-加载（ETL）与术语映射** - 本模块教授学习者在数据和术语映射中进行提取、转换和加载（ETL）数据的过程与挑战，以及实际案例。 4. **技术：数据质量评估** - 通过回顾数据质量的挑战、使用的数据质量度量以及评估其可接受性的数据质量规则，探讨数据质量的各个维度。 5. **实践应用：创建ETL流程，将MIMIC-III表转换为OMOP** - 本模块综合所学知识，完成一个真实的实践练习，使用ETL方法将MIMIC3数据转换为OMOP通用数据模型。
Name:Introduction: Clinical Data Models and Common Data Models
Description:This week describes clinical data models and explains the need for and use of common data models in national and international data networks. We will also cover the features of Entity-Relationship Diagrams (ERDs) to describe the key technical features of data models.
Name:Tools: Querying Clinical Data Models
Description:We take a deep dive into the technical features of clinical data models using MIMIC3 as our example and research common data models using OMOP as our example.
Name:Techniques: Extract-Transform-Load and Terminology Mapping
Description:This module teaches learners about the processes and challenges with extracting, transforming and loading (ETL) data with real-world examples in data and terminology mapping.
Name:Techniques: Data Quality Assessments
Description:We explore the dimensions of data quality by reviewing its challenges, data quality measurements used to measure it, and data quality rules to assess its acceptability for use.
Name:Practical Application: Create an ETL Process to Transform a MIMIC-III Table to OMOP
Description:In this module, you gather everything you’ve learned to complete a real-world hands-on exercise using ETL methods to convert MIMIC3 data into the OMOP common data model.
This course aims to teach the concepts of clinical data models and common data models. Upon completion of this course, learners will be able to interpret and evaluate data model designs using Entity-Relationship Diagrams (ERDs), differentiate between data models and articulate how each are used to support clinical care and data science, and create SQL statements in Google BigQuery to query the MIMIC3 clinical data model and the OMOP common data model.
