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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/the-data-science-of-health-informatics
课程评论：没有评论
课程名称：健康信息学的数据科学 课程概述： 健康数据以其种类繁多、复杂程度高和处理精确性要求严苛而著称。这些数据不仅服务于产生它们的患者的治疗，还可用于其他多种用途。例如，健康数据的二次使用包括人口健康（如，哪些人群需要更多关注）、研究（如，哪些药物在实际中更有效）、质量评估（如，机构是否达到了基准标准）及转化研究（如，新技术是否得到了合适的应用）。通过本课程的学习，学生将能够识别不同类型的健康和医疗数据，清晰并完整地表达问题，解读针对EHR数据二次使用的查询，并理解这些查询的结果。 课程大纲： 1. **数据库及数据类型介绍**： 本模块将定义数据库并在临床信息学的背景下探讨其作用。我们将介绍常见的健康数据类型，如人口统计、诊断、药物、程序和利用数据。此外，还将回顾新兴健康数据，如实验室订单/结果、生命体征、社会数据及患者生成的数据。 2. **数据来源与数据挑战**： 本模块回顾从保险索赔和电子健康记录中提取的数据规范。我们将讨论使用健康数据的常见挑战，特别是数据质量、数据互操作性和数据系统架构的问题。最后，我们将描述健康数据的“大数据”挑战，解释可能妨碍分析工作的一些数据问题。 3. **数据问题的形成**： 在了解可用数据的基础上，我们将探讨如何将你和同事的问题转化为数据库可以理解的查询。除了提供一些翻译的经验法则外，你还将接触到三个在线工具，以测试这些技能。同时，你将观看约翰霍普金斯企业数据仓库的数据经理Sam Meiselman的访谈，他每天都需要运用这些技能。 4. **健康信息学中数据科学的实际应用**： 为了强调将问题翻译为查询的挑战与艺术，本模块将有两位专业人士的访谈。他们分别来自数据管理和所属领域，提供了相似（理解数据查询目的的必要性）与不同（可用数据的多样性与领域问题的复杂性）两方面的观点。 通过本课程的学习，学员将获得深入的健康信息学与数据科学的理解，掌握如何有效利用健康数据进行分析和决策。
Name:Introduction to Databases and Data Types
Description:In this module, we will begin by introducing and defining databases, and placing the role of databases within the context of clinical informatics. We will continue by introducing the common health data types such as demographics, diagnosis, medications, procedures, and utilization data. We will finish this module by reviewing the emerging health data such as lab orders/results, vital signs, social data, and patient-generated data.
Name:Data Sources and Data Challenges
Description:In this module, we review the data specifications extracted from insurance claims and electronic health records. We will then discuss the common challenges in using health data, specifically issues with data quality, data interoperability, and data system architectures. Finally, we will describe the “Big Data” challenges of health data and explain some of the data problems that may hinder analytical efforts.
Name:Formulating Data Questions
Description:With this understanding of the data available, it’s time to see how to turn questions you and your colleagues will have into queries the database can understand. Besides getting rules of thumb for doing this translation, you will also be introduced to three online tools available to test some of these skills. You will also watch an interview with Sam Meiselman, course instructor and the data manager in charge of the Johns Hopkins Enterprise Data Warehouse, who has to use these skills on a daily basis.
Name:Real World Applications of Data Science in Health Informatics
Description:To send home the recurring message on the challenges and art of translating questions into queries, you will see interviews with two professionals: One who comes from the data management side of the equation, and one who comes from the domain. They will give you perspectives that are both similar (the need to understand the problem for which the data are being retrieved) and different (the multiplicity of data available vs the richness of the domain problem).
Health data are notable for how many types there are, how complex they are, and how serious it is to get them straight. These data are used for treatment of the patient from whom they derive, but also for other uses. Examples of such secondary use of health data include population health (e.g., who requires more attention), research (e.g., which drug is more effective in practice), quality (e.g., is the institution meeting benchmarks), and translational research (e.g., are new technologies being applied appropriately). By the end of this course, students will recognize the different types of health and healthcare data, will articulate a coherent and complete question, will interpret queries designed for secondary use of EHR data, and will interpret the results of those queries.
