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HST.95J/683J医学决策支持

HST.951J / 6.873J Medical Decision Support
课程网址: http://ocw.mit.edu/courses/health-sciences-and-technology/hst-951...  
主讲教师: Isaac Kohane ; Lucila Ohno-Machado ; Peter Szolovits ; Staal Vinterbo
开课单位: 麻省理工学院
开课时间: 信息不详。欢迎您在右侧留言补充。
课程语种: 英语
中文简介:
本课程主要介绍了决策分析、人工智能和预测模型构建与评价等概念在具体的医学应用背景下的应用。它强调了在实际系统中使用这些方法的优点和缺点，并提供了实践经验。它的技术重点是决策支持、基于知识的系统(定性和定量)、学习系统(包括逻辑回归、分类树、神经网络、粗糙集)和评价这些系统性能的技术。它审查以计算机为基础的诊断、规划和治疗干预措施的监测。本文还讨论了已实现的医学应用程序及其构建中使用的软件工具。学生根据实际临床资料，运用本课程所学的机器学习方法，完成期末专题报告。讲师:Stephan Dreiseitl教授Ju Jan Kim教授Bill Long教授Marco Ramoni教授Fred Resnic教授David Wypij教授
课程简介: This course presents the main concepts of decision analysis, artificial intelligence and predictive model construction and evaluation in the specific context of medical applications. It emphasizes the advantages and disadvantages of using these methods in real-world systems and provides hands-on experience. Its technical focus is on decision support, knowledge-based systems (qualitative and quantitative), learning systems (including logistic regression, classification trees, neural networks, rough sets), and techniques to evaluate the performance of such systems. It reviews computer-based diagnosis, planning and monitoring of therapeutic interventions. It also discusses implemented medical applications and the software tools used in their construction. Students produce a final project using the machine learning methods learned in the course, based on actual clinical data. Lecturers Prof. Stephan Dreiseitl Prof. Ju Jan Kim Prof. Bill Long Prof. Marco Ramoni Prof. Fred Resnic Prof. David Wypij
关 键 词: 决策分析; 人工智能; 预测模型; 医疗软件; 决策支持; 知识库系统; 学习系统; Logistic回归; 分类树; 神经网络; 粗糙集
课程来源: 麻省理工学院公开课
最后编审: 2018-06-16：cmh
阅读次数: 24

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