医学院校非信息类专业医学信息学选修课的教学实践研究

以移动互联网、物联网、云计算、大数据等为代表的新一代信息技术与健康医疗行业紧密融合,对医学院校面向非信息类专业的医学信息学选修课提出了更高的教学目标和内容要求。本文结合实际教学需求和临床医学生培养目标,对医学信息学选修课的教学内容进行了科学地优化和配置。选择医学信息学教育中的“小数据集,电子病历/电子健康档案的基本应用”为主要内容,模块化医学信息处理中的数值型数据、文本型数据和图像数据,以案例驱动和翻转课堂变革教学方式,将机器学习中常见的方法（如回归、分类和聚类方法）根植于案例的解决方案中,以综合的算法实例报告作为课程考核评价方式。实践表明,以上探索既激发了学生对医学信息学选修课的兴趣,又有效贯彻了医学生培养早预测、早调整、早发现、早诊断和早治疗的“五早”模式。

Teaching practice and exploration of the optional course of medical informatics for non-informatics students in medical colleges and universities

Nowadays, the new healthcare industry is closely integrated with the new-generation information techniques such as mobile Internet, Internet of things, cloud computing, and big data, which proposes higher requirements for the teaching objectives and contents of the optional course of medical informatics for non-informatics students in medical colleges and universities. Based on the actual teaching demands and the training objectives of clinical medical students, the teaching contents of the optional course of medical informatics were scientifically optimized and allocated. With "small datasets and basic application of electronic medical records/electronic health records" in medical informatics education as the main contents, numeric data, text data, and image data were modularized in medical information processing, and the methods of flipped classroom and case-driven teaching were adopted. The commonly used methods in machine learning (such as regression, classification, and clustering methods) were introduced and applied in task-based case studies, and the comprehensive algorithm case report was used for course assessment and evaluation. The teaching practice has shown that the above exploration not only stimulated the interest in the optional course of medical informatics among students, but also effectively implemented the "five early" mode of early prediction, early adjustment, early identification, early diagnosis, and early treatment among medical students.