除了数学,统计和编程:数据科学,机器学习和人工智能能力和课程为临床医生,计算机专家,科学记者和研究人员
William R Hersh1, Robert E Hoyt2, Steven Chamberlin1
1Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
Health systems (Basingstoke, England)
|October 20, 2023
概括
这项研究为医疗保健专业人员提出了可访问的数据科学课程,超越了传统的编程重型方法. 这些策略旨在增加对数据科学在医疗保健中的应用的参与和理解.
科学领域:
- 生物医学数据科学 生物医学数据科学
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 当前的生物医学数据科学培训通常需要先进的编程和统计数据,这限制了许多医疗保健专业人员的可访问性.
- 传统的方法适合研究生,但排除了临床医生和计算机专家等关键利益相关者.
- 需要在各种医疗保健角色中更广泛地参与数据科学.
研究的目的:
- 描述针对不同医疗保健利益相关者量身定制的数据科学培训的替代课程策略.
- 专注于与临床医生,计算机专家,科学记者和研究人员相关的实际问题解决.
- 扩大对生物医学数据科学有知识和参与的人群.
主要方法:
- 开发以直观编程工具为中心的课程,以最小的编码进行数据探索.
- 包括数据建模,结果评估和数据科学研究的批判性评估的模块.
- 专注于与目标利益相关方受众相关的现实问题.
主要成果:
- 确定了关键能力,包括直观的数据探索,数据建模和分析评估.
- 拟议的战略,以适应不同级别的编程专业知识.
- 突出了通过可访问的培训实现更广泛的利益相关者参与的潜力.
结论:
- 可访问的数据科学教育可以赋予更广泛的医疗保健专业人员权力.
- 重点关注实际应用和直观工具的课程调整至关重要.
- 扩大数据科学知识可以增强其在整个医疗保健生态系统的影响.
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