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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cdss1
课程评论：没有评论
课程名称：临床数据库的数据挖掘 - CDSS 1 课程概述：本课程将介绍MIMIC-III，这是最大的公共电子健康记录（EHR）数据库，可用于基准测试机器学习算法。特别是，您将学习该关系数据库的设计，查询、提取和可视化描述性分析的工具。理解数据库的架构和国际疾病分类编码对于如何将研究问题映射到数据以及提取关键临床结果，从而开发出临床有用的机器学习算法至关重要。 课程大纲： 1. **电子健康记录与公共数据库** - 本模块将介绍MIMIC-III，这是最大的公共电子健康记录（EHR）数据库，用于基准测试机器学习算法。您将学习此关系数据库的设计以及可用于查询、提取和可视化描述性分析的工具。了解架构和国际疾病分类编码的重要性，以便将研究问题映射到数据和提取关键临床结果。 2. **MIMIC III作为关系数据库** - 本周讨论MIMIC III数据库的基本结构并进行实用练习，学习如何提取和可视化汇总统计信息。我们将理解定义临床结果的困难，并检查与特定患者相关的临床变量。 3. **国际疾病分类系统** - 本周讨论国际疾病分类（ICD）系统的历史，该系统是经过协作发展，使死亡证明中的医学术语和信息能够为统计目的进行分组。通过实际示例学习如何从MIMIC III数据库中提取ICD-9编码并进行可视化。此外，我们还讨论ICD-9、ICD-10和ICD-11系统之间的差异。 4. **MIMIC-III中的概念及患者纳入流程图示例** - 本周包括对临床概念的概述，这些概念是提供疾病评分的统计工具。它们基于专家意见开发，并在数据驱动方法的基础上扩展。这些模型是精准医学机器学习模型的前身。最后，本周的实用练习提供了实现复杂患者纳入流程图的机会。
Name:Electronic Health Records and Public Databases
Description:This module will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics. The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.
Name:MIMIC III as a relational database
Description:This week includes a discussion of the basic structure of MIMIC III database and practical exercises on how to extract and visualise summary statistics. We will understand the difficulty in defining clinical outcomes and we are going to examine clinical variables related to a specific patient.
Name:International Classification of Disease System
Description:This week discusses the history of the International Classification of Diseases (ICD) system, which has been developed collaboratively so that the medical terms and information in death certificates can be grouped together for statistical purposes. Practical examples shows how to extract ICD-9 codes from MIMIC III database and visualise them. Furthermore, we discuss differences between ICD-9, ICD-10 and ICD-11 systems.
Name:Concepts in MIMIC-III and an example of patients inclusion flowchart
Description:This week includes an overview of clinical concepts, which are statistical tools to provide illness scores. They are developed based on expert opinion and subsequently extended based on data-driven methods. These models are the precursor of machine learning models for precision medicine. Finally, the practical exercises of this week provides the opportunity to implement a complex flowchart of patients inclusion.
This course will introduce MIMIC-III, which is the largest publicly Electronic Health Record (EHR) database available to benchmark machine learning algorithms. In particular, you will learn about the design of this relational database, what tools are available to query, extract and visualise descriptive analytics. The schema and International Classification of Diseases coding is important to understand how to map research questions to data and how to extract key clinical outcomes in order to develop clinically useful machine learning algorithms.
