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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/population-health-predictive-analytics
课程评论：没有评论
课程名称：人口健康：预测分析 概述：预测分析在医学领域有着悠久的传统。开发更好的预测模型是改善医疗保健的关键步骤，这些工具能够指导我们在预防措施和个性化治疗方面的决策。为了有效地使用和开发这些模型，我们必须更好地理解它们。在本课程中，您将学习如何制作准确的预测工具，以及如何评估它们的有效性。首先，我们将讨论预测分析在预防、诊断和效果评估中的作用。然后，我们将探讨一些关键概念，如研究设计、样本大小和过拟合。 此外，我们还将全面讨论模型开发中的重要问题，如缺失值、非线性关系和模型选择。我们将分析偏差-方差权衡及其在预测中的作用。最后，我们将探讨多种评估模型的方法，包括性能度量，以及评估模型的内部和外部有效性。课程还将介绍如何将模型更新到特定环境。 在整个课程中，我们将使用R语言来说明所讲述的概念。您无需在计算机上安装R软件即可参与本课程，您将在Coursera环境中访问R及所有示例数据集。我们也提供了一些建议，供您在进行特定类型分析时安装使用更多的软件包。 每个模块还可能包含实践测验题。无论您回答正确与否，您都将及格。通过思考问题的答案后再查看正确答案和解释，您将获得更多的学习。 本课程是莱顿大学人口健康管理硕士项目的一部分（当前正在开发中）。 课程大纲： 1. 欢迎来到莱顿大学：介绍课程内容和学习社区。 2. 预防、诊断和效果的预测：讨论预测分析在这些方面的作用和框架。 3. 建模概念：介绍预测建模的一些关键概念，如研究设计和样本大小等。 4. 模型开发：解决缺失值、非线性关系、模型选择等问题，并介绍LASSO和Ridge回归等先进方法。 5. 模型验证与更新：评估预测模型的质量，探讨内部与外部有效性以及如何将模型更新到特定医疗环境。 希望您能享受这门课程，获得关于预测分析的深入理解。
Name:Welcome to Leiden University
Description:Welcome to the course Predictive Analytics! We are excited to have you in class and look forward to your contributions to the learning community. To begin, we recommend taking a few minutes to explore the course site. Review the material we will cover each week, and preview the assignments you will need to complete in order to pass the course. Click Discussions to see forums where you can discuss the course material with fellow students taking the class. If you have questions about course content, please post them in the forums to get help from others in the course community. For technical problems with the Coursera platform, visit the Learner Help Center. Good luck as you get started, and we hope you enjoy the course!
Name:Prediction for prevention, diagnosis, and effectiveness
Description:In this module, we discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. We begin with a brief introduction to predictive analytics, which we follow by differentiating between population-based and targeted interventions. We then explain why and when it may be beneficial to test for a diagnosis, and how analytic tools can help inform these decisions. Finally, we focus on the balance between benefits and harms of a certain treatment, and how we can predict the benefit for an individual.
Name:Modeling Concepts
Description:In this module, we will present some key concepts in prediction modeling. First, we weigh the strengths and weakness of various study designs. Second, we stress the importance of an appropriate sample size for reliable inference. Then, we discuss the issues of overfitting a prediction model, and regression-to-the-mean. Finally, we will guide you through the popular bootstrap procedure, showing how it can be used to assess parameter variability.
Name:Model development
Description:In this module, we focus on model development. First, we turn our attention to the missing values problem. We discuss well-known missingness mechanisms, and methods to deal with missing values appropriately. Second, we learn about methods to deal with non-linearity in a dataset. We then address the topic of model selection, focusing on the limitations of traditional stepwise selection procedures. Last, we talk about how introducing bias in exchange for lower variance can improve prediction quality. This can be done by using advanced methods, such as LASSO and Ridge regression.
Name:Model validation and updating
Description:In this final module, we learn about assessing the quality of a prediction model. First, we extensively discuss standard performance measures for both binary and continuous outcomes. Second, we explore different ways of validating a prediction model. We look at how to assess both the internal, and the more relevant external validity of a model. Next, we will look at how to update a model and make it applicable to a specific medical setting. We conclude with an interview, where we more broadly discuss the potential of predictive analytics by taking the example of the island of Aruba.
Predictive analytics has a longstanding tradition in medicine. Developing better prediction models is a critical step in the pursuit of improved health care: we need these tools to guide our decision-making on preventive measures, and individualized treatments. In order to effectively use and develop these models, we must understand them better. In this course, you will learn how to make accurate prediction tools, and how to assess their validity. First, we will discuss the role of predictive analytics for prevention, diagnosis, and effectiveness. Then, we look at key concepts such as study design, sample size and overfitting. Furthermore, we comprehensively discuss important modelling issues such as missing values, non-linear relations and model selection. The importance of the bias-variance tradeoff and its role in prediction is also addressed. Finally, we look at various way to evaluate a model - through performance measures, and by assessing both internal and external validity. We also discuss how to update a model to a specific setting. Throughout the course, we illustrate the concepts introduced in the lectures using R. You need not install R on your computer to follow the course: you will be able to access R and all the example datasets within the Coursera environment. We do however make references to further packages that you can use for certain type of analyses – feel free to install and use them on your computer. Furthermore, each module can also contain practice quiz questions. In these, you will pass regardless of whether you provided a right or wrong answer. You will learn the most by first thinking about the answers themselves and then checking your answers with the correct answers and explanations provided. This course is part of a Master's program Population Health Management at Leiden University (currently in development).
