对长形式医院课程总结的忠诚度指标的元评估
Griffin Adams1, Jason Zucker2, Noémie Elhadad3
1Computer Science, Columbia University, New York, New York, US.
概括
评估用于临床总结的自然语言处理 (NLP) 模型需要强大的忠实度指标. 我们的研究使用人类注释对这些指标进行了基准分析,为改善临床注释总结提供了洞察力.
科学领域:
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 长形式的临床总结有助于临床医生和患者.
- 事实一致性 (忠实性) 对于安全的临床摘要使用至关重要.
研究的目的:
- 对于NLP生成的临床摘要的基准可信度指标.
- 通过细粒度的人类注释来评估度量适用性和限制.
- 评估临床文本的域调整和对齐策略.
主要方法:
- 为艾滋病毒患者创建了患者入院和总结的病体.
- 人类注释者将摘要元素 (实体,动作) 分类为"不正确"",缺失"或"不在注释中".
- 通过将得分与临床医生评级相关联,对NLP忠实度指标进行了元评估.
主要成果:
- 现成的指标与临床医生的评分有很好的相关性,但可能过度依赖于直接的文本复制.
- 域调整 (预训练,微调) 影响度量表现.
- 当一次评估一个总结句子,同时使用最小的配套注释时,指标表现最好.
结论:
- 现有的忠实度指标对临床总结有希望,但需要仔细应用.
- 人类注释为临床NLP任务提供了必要的细粒度评估.
- 优化指标评估策略可以提高自动化临床摘要的可靠性.
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