自动化患者笔记评分:检查评分可靠性和可行性
William F Bond1, Jianing Zhou2, Suma Bhat3
1W.F. Bond is professor, Department of Emergency Medicine, University of Illinois College of Medicine, Peoria, Illinois, and is affiliated with Jump Simulation, an OSF HealthCare and University of Illinois College of Medicine at Peoria Collaboration; ORCID: http://orcid.org/0000-0001-6714-7152.
概括
使用自然语言处理 (NLP) 来自动评分患者笔记,为医学学生提供可靠和可行的反. 这种自动化简短答案分级 (ASAG) 系统增强了基于病例的医学教育中的学习.
科学领域:
- 医学教育 医学教育
- 自然语言处理自然语言处理.
- 心理测量 心理测量 心理测量
背景情况:
- 评分接触后的患者笔记 (PNs) 提供了有价值的学生绩效见解,但资源密集型.
- 在NLP和机器学习方面的进步使得PNS的自动化简短答案分级 (ASAG) 成为可能.
研究的目的:
- 评估ASAG系统的心理测量特征和可靠性,以评分PNS.
- 评估在新案例中实施ASAG的可行性和注释要求.
主要方法:
- 使用feed-forward神经网络算法对来自标准化患者 (SP) 病例的PNS训练ASAG系统.
- 每个案例需要10个PNS的教师注释来调整系统.
- 使用项目分析和评级者间可靠性 (IRR) 度量 (kappa) 将ASAG评级与人类评级进行了比较.
主要成果:
- 与人类评分相比,ASAG评分显示了与学习者表现和高IRR (kappa = .83) 的区别的变化.
- 该系统表现出高度的项目区分和内部一致性可靠性 (克朗巴赫α = .65.77).
- 调整ASAG系统的成本效益很高,教师的时间每例约为928美元.
结论:
- 基于NLP的PN的ASAG对于学习者反是可靠的,并且在心理测量上是合理的.
- 需要最小的句子级注释来调整系统以适应新案例,提高可行性.
- 在基于病例的医学教育中,ASAG具有改善反的巨大潜力.
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