Hersh William R, Hoyt Robert E, Chamberlin Steven, Ancker Jessica S, Gupta Aditi, Borlawsky-Payne Tara B
Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
Department of Medicine, Virginia Commonwealth University, Richmond, VA, USA.
Health Syst (Basingstoke). 2023 Jul 18;12(3):255-263. doi: 10.1080/20476965.2023.2237745. eCollection 2023.
Data science, machine learning and artificial intelligence applications impact clinicians, informaticians, science journalists, and researchers. Most biomedical data science training focuses on learning a programming language in addition to higher mathematics and advanced statistics. This approach is appropriate for graduate students but greatly reduces the number of individuals in healthcare who can be involved in data science. To serve these four stakeholder audiences, we describe several curricular strategies focusing on solving real problems of interest to these audiences. Relevant competencies for these audiences include using intuitive programming tools that facilitate data exploration with minimal programming background, creating data models, evaluating results of data analyses, and assessing data science research reports, among others. Offering the curricula described here more broadly could broaden the stakeholder groups knowledgeable about and engaged in data science.
数据科学、机器学习和人工智能应用对临床医生、信息学家、科学记者和研究人员产生影响。大多数生物医学数据科学培训除了高等数学和高级统计学外，还侧重于学习一种编程语言。这种方法适用于研究生，但大大减少了医疗保健领域中能够参与数据科学的人员数量。为了服务这四类利益相关者群体，我们描述了几种课程策略，重点是解决这些群体感兴趣的实际问题。这些群体的相关能力包括使用直观的编程工具，在几乎没有编程背景的情况下促进数据探索、创建数据模型、评估数据分析结果以及评估数据科学研究报告等。更广泛地提供这里描述的课程可以扩大了解并参与数据科学的利益相关者群体。
