使用自然语言处理和机器学习来识别应用程序中的内科-儿科住院价值
Benjamin Drum1, Jianlin Shi2, Bennet Peterson3
1B. Drum is assistant professor, Department of Internal Medicine, and adjunct professor, Department of Pediatrics, University of Utah School of Medicine, Salt Lake City, Utah.
概括
机器学习模型 (MLM) 可以通过从应用叙述中识别关键值来帮助居住计划选申请人. 这种方法提供了适度的灵敏度和高的特异性,有助于高效和潜在的不太偏见的候选人评价,以选择居留.
科学领域:
- 医学教育 医学教育
- 计算医学是一种计算医学.
- 医疗信息学 医疗信息学
背景情况:
- 整体审查在住院招生是有效的,但耗时.
- 传统的查依赖于定量指标,冒着社会经济和种族偏见的风险.
- 非结构化的应用数据需要大量的资源来进行评估.
研究的目的:
- 开发和评估一个机器学习模型 (MLM) 用于选住院申请人.
- 评估MLM从应用叙述中识别与居民成功相关的关键价值的能力.
- 将MLM业绩与手动整体审查进行比较.
主要方法:
- 从内部医学-儿科住院申请 (2015-2019) 的叙事部分提取的文本片段.
- 专家评审人员为10个预定义值 (例如学术实力,同情心,团队合作) 标注了片段.
- 展望地将训练有素的MLM应用于2023年的应用程序,并将结果与手动整体审查进行了比较.
主要成果:
- 该MLM表现出中度敏感性 (0.64) 和高特异性 (0.97).
- 来自MLM的关键应用指标与面试邀请有显著的相关性 (P < .001).
- 十个评估值中的八个是面试状态的显著预测指标.
结论:
- 一个MLM可以有效地确定在内部医学-儿科计划中居民成功的重要价值观.
- 开发的MLM提供了一个潜在的更有效和客观的选工具.
- 未来的工作包括通过增加注释和参数调整来改进MLM.
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