解释护理需求评估调查:对最先进的本地和全球可解释的人工智能方法的定性和定量评估
Necip Oğuz Şerbetci1, Stefan Blüher2, Paul Gellert2
1Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin 10099, Germany.
JAMIA open
|July 31, 2025
概括
可解释AI (XAI) 方法有助于分析应用程序的护理需求. 变压器模型在预测护理需求和确定关键影响因素方面略高于传统方法.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
背景情况:
- 越来越多的老年人群需要更好的护理需求评估.
- 了解影响护理需求的因素对于最佳支持至关重要.
- 目前分析护理福利申请的方法可能耗时.
研究的目的:
- 为了探索影响护理需求的因素,使用大量的护理福利应用程序.
- 将可解释的人工智能 (XAI) 方法用于从文本数据中提取见解进行比较.
- 支持专家收集可靠的护理需求评估标准.
主要方法:
- 使用了超过72,000个德国护理福利申请.
- 训练过的变压器模型可以预测护理需求评估结果.
- 应用特征归属方法 (XAI) 来提取影响预测的关键短语.
- 对不同护理模块和重症级别的全球洞察力进行汇总的本地解释.
主要成果:
- 基于变压器的模型在预测护理需求方面略有改善了词汇袋基线.
- 传统和基于变压器的XAI方法都发现了有用的护理相关短语.
- 变压器解释更好地平衡了罕见和常见的短语,并改进了对正确评估模块的分配.
结论:
- 在不需要额外的注释的情况下,XAI为大型文本体提供了宝贵的见解.
- 传统的和基于深度学习的XAI方法都对文本分析有好处.
- 建议使用组合方法来全面理解大型文本数据集.
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