Business Application of Machine Learning and Artificial Intelligence in Healthcare

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课程主页: https://www.coursera.org/archive/artificialintelligence-in-healthcare

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课程大纲

Decision Support and Use Cases
Predictive Modeling Basics
Consumerism and Operationalization
Advanced Topics in Operationalization

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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.

机器学习和人工智能在医疗保健中的商业应用:医疗保健的未来正变得取决于我们将机器学习和人工智能集成到我们组织中的能力。但是,仅仅认识到人工智能的机会还不够。作为医疗保健行业的领导者,我们必须首先确定这些应用程序的最佳用途,以确保我们将投资重点放在解决影响底线的问题上。 在这四个模块中,我们将研究决策支持,旅程映射,预测分析以及将机器学习和人工智能嵌入医疗保健行业的用途。在本课程结束时,您将能够: 1.确定决策支持中涉及的因素,这些因素可以改善整个提供者/付款者生态系统的业务绩效。 2.通过在现实世界中应用行程映射和痛点分析来确定医疗保健业务应用的机会。 3.通过案例研究确定方法和技术的差异,以便适当地适用于疼痛点。 4.认真评估利用决策支持来适应行业趋势的机会。

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