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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/artificialintelligence-in-healthcare
课程评论：没有评论
课程名称：医疗领域的机器学习和人工智能的商业应用 课程概述：随着医疗保健未来越来越依赖于将机器学习和人工智能整合到组织中，作为行业领导者，我们必须首先确定这些应用的最佳使用方法，确保我们的投资专注于解决影响业绩的问题。在本课程的四个模块中，我们将探讨决策支持、用户旅程映射、预测分析以及如何将机器学习和人工智能嵌入医疗行业。完成本课程后，您将能够： 1. 确定决策支持中涉及的因素，以改善提供者/支付者生态系统中的业务表现。 2. 通过在实际环境中应用用户旅程映射和痛点分析，识别医疗保健中的商业应用机会。 3. 辨别不同的方法和技术，以适当地应用于痛点案例研究。 4. 批判性地评估在适应行业趋势中利用决策支持的机会。 课程大纲： 模块一：决策支持与案例分析 描述：快速变化的技术正在影响现代社会的各个方面，医疗行业也不例外。本模块讨论如何应对这些变化，包括识别痛点和决策支持的潜在应用。 模块二：预测建模基础 描述：本模块将探讨如何预测健康结果和成本，使用机器学习降低医疗成本所需的数据和分析方法，及其对组织和病患的影响。 模块三：消费者行为与运营化 描述：本模块将分析不同预测模型的适用性，讨论如何结合适当的决策支持方法，改善整体的外展与生产力，并降低成本。 模块四：运营化的高级主题 描述：本模块强调不仅要预测，还要开出建议。通过分析改善病患体验和健康状态的指导和建议，提升医疗专业人员的决策能力。 通过本课程，学员将充分理解如何将机器学习与人工智能应用于医疗行业，以解决实际问题并提升业务绩效。
Name:Decision Support and Use Cases
Description:Rapid changes in technology are impacting every facet of modern society, and the healthcare industry is no exception. Navigating these changes is crucial, whether you are currently working in the industry, hoping to step into a new role, or are simply interested in how technology is being used in healthcare. No doubt you have heard the terms, “machine learning” and “artificial intelligence” more frequently in the last few years - but what does this mean for you, or the healthcare industry in general? Keeping up with the changing trends, examining the potential use of decision support, and identifying some of the pain points that can be addressed, are some of the topics we’ll be discussing in this Module.
Name:Predictive Modeling Basics
Description:Let’s navigate through what it takes to predict health outcomes and cost. What if we could use machine learning in your organization to reduce the cost of care for both the organization and the members receiving that care? Have you thought about what data you need to collect? How you might need to enrich that data to gain more insight in to what is driving those outcomes and cost? Or what types of machine learning algorithms you might utilize in order to most effectively target patients who are likely to be high cost? We are going to look at not only the tech behind the predictions, but also examine the business and data relationships within the healthcare industry that ultimately impact your ability to deliver an effective solution.
Name:Consumerism and Operationalization
Description:Now that we have discussed various types of predictive models, let’s take a look at which models are appropriate for the business case we are trying to address and how we can evaluate their performance. For example, is using the same performance metric appropriate to use when making predictions about individual vs. population health? In this module we'll discuss how layering appropriate decision support methods on top of predictive analytics and machine learning can lay the groundwork for significant improvements in overall outreach and productivity, as well as decrease costs. Finally, we will discuss the key to blending decision support into the existing ecosystem of your business workflow and technology infrastructure.
Name:Advanced Topics in Operationalization
Description:Now that we know the importance of decision support and predictive modeling, we are going to take that one step further. Not only do we need to predict, but more importantly, we need to prescribe. It is not enough to just implement alerts and reminders - we need to offer guidance and recommendations for healthcare professionals. Let’s take a look at how analytics can improve the patient experience and their overall health status.
The future of healthcare is becoming dependent on our ability to integrate Machine Learning and Artificial Intelligence into our organizations. But it is not enough to recognize the opportunities of AI; we as leaders in the healthcare industry have to first determine the best use for these applications ensuring that we focus our investment on solving problems that impact the bottom line. Throughout these four modules we will examine the use of decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into the healthcare industry. By the end of this course you will be able to: 1. Determine the factors involved in decision support that can improve business performance across the provider/payer ecosystem. 2. Identify opportunities for business applications in healthcare by applying journey mapping and pain point analysis in a real world context. 3. Identify differences in methods and techniques in order to appropriately apply to pain points using case studies. 4. Critically assess the opportunities to leverage decision support in adapting to trends in the industry.
