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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/computational-phenotyping
课程评论：没有评论
课程名称：识别患者群体 课程概述：本课程教授计算表型学的基础知识，这是一种用于识别患者群体的生物医学信息学方法。您将学习不同临床数据类型在识别特定疾病或特征患者时的表现。同时，您将学习如何编写不同的数据操作和组合，以增加算法的复杂性并提高其性能。课程的最后，您将有机会通过一个实际应用来测试您的技能，开发一个计算表型算法，以识别高血压患者。此工作将使用一个真实的临床数据集，并通过我们行业合作伙伴Google Cloud提供的在线计算环境进行。 课程大纲： 1. 引言：识别患者群体 - 描述：了解计算表型学及其在识别患者群体中的应用。 2. 工具：临床数据类型 - 描述：理解不同临床数据类型如何用于识别患者群体，并开始开发一个识别2型糖尿病患者的计算表型算法。 3. 技术：数据操作与组合 - 描述：学习如何操作单一数据类型并在计算表型算法中组合多个数据类型，从而开发一个更复杂的算法来识别2型糖尿病患者。 4. 技术：算法选择与可移植性 - 描述：了解如何选择单一“最佳”计算表型算法，并最终确定一个用于2型糖尿病的表型算法。 5. 实际应用：开发计算表型算法以识别高血压患者 - 描述：将新技能付诸实践，开发算法识别高血压患者。 这门课程为希望掌握计算表型学并应用于临床数据分析的学习者提供了全面的知识和实践机会。
Name:Introduction: Identifying Patient Populations
Description:Learn about computational phenotyping and how to use the technique to identify patient populations.
Name:Tools: Clinical Data Types
Description:Understand how different clinical data types can be used to identify patient populations. Begin developing a computational phenotyping algorithm to identify patients with type II diabetes.
Name:Techniques: Data Manipulations and Combinations
Description:Learn how to manipulate individual data types and combine multiple data types in computational phenotyping algorithms. Develop a more sophisticated computational phenotyping algorithm to identify patients with type II diabetes.
Name:Techniques: Algorithm Selection and Portability
Description:Understand how to select a single "best" computational phenotyping algorithm. Finalize and justify a phenotyping algorithm for type II diabetes.
Name:Practical Application: Develop a Computational Phenotyping Algorithm to Identify Patients with Hypertension
Description:Put your new skills to the test - develop an computational phenotyping algorithm to identify patients with hypertension.
This course teaches you the fundamentals of computational phenotyping, a biomedical informatics method for identifying patient populations. In this course you will learn how different clinical data types perform when trying to identify patients with a particular disease or trait. You will also learn how to program different data manipulations and combinations to increase the complexity and improve the performance of your algorithms. Finally, you will have a chance to put your skills to the test with a real-world practical application where you develop a computational phenotyping algorithm to identify patients who have hypertension. You will complete this work using a real clinical data set while using a free, online computational environment for data science hosted by our Industry Partner Google Cloud.
