验证由自我组织地图所代表的知识,并具有专家衍生知识结构
Andrew James Amos1, Kyungmi Lee2, Tarun Sen Gupta3
1College of Medicine & Dentistry, James Cook University, Townsville, Australia. Andrew.Amos@jcu.edu.au.
BMC medical education
|April 16, 2024
概括
像MedSOM这样的机器学习可视化可以通过连贯地总结教科书参考来验证精神病学知识领域. 这增强了对人工智能驱动的医学教育见解的理解和信任.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 知识表示 知识表示
背景情况:
- 在医疗保健中采用机器学习 (ML) 的障碍是缺乏可解释性.
- 医学文献的可视化可以提炼大量的信息,但往往缺乏清晰的含义.
- 验证ML衍生的见解对于它们在医学课程开发等领域的接受至关重要.
研究的目的:
- 为了验证自我组织地图 (MedSOM) 可视化的解释性.
- 评估MedSOM能够连贯地总结精神病学知识的能力.
- 将ML输出与精神病学中已建立的知识标准联系起来.
主要方法:
- 在Medline/PubMed索引文章上训练了一个自我组织的地图 (MedSOM).
- 分析了核心精神病学教科书的十个版本的参考清单.
- 应用K-means集群对在MedSOM上投射的教科书参考进行了分析.
主要成果:
- 在10个教科书版本 (1967-2017年) 中,MedSOM始终确定了六个不同的精神病学知识领域.
- 聚类揭示了在广泛的精神病学实践领域的层面上连贯的组织.
- 确定的领域包括一般/成人精神病学,儿童精神病学和行政精神病学.
结论:
- 这项研究证实了MedSOM能够代表和稳定精神病学知识领域的能力.
- 这表明了验证医学文献的ML驱动可视化方法.
- 成功验证增强了信任,并促进了在医学教育和课程开发中使用ML见解.
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