用信息检索提升乳腺癌淋巴水肿预测的可解释性
Enhancing the Interpretability of Rule-based Explanations through Information Retrieval
- 通过信息检索指标分析规则模型中各风险因素的重要性
- 用户研究显示新方法显著提升预测结果的可读性和实用性
- 适合医疗AI决策支持系统中的可解释性改进
数据驱动的人工智能技术因缺乏透明性，限制了其在医疗决策中的应用。本文针对乳腺癌患者接受淋巴结放疗后上肢淋巴水肿的风险评估，提出一种基于归因的信息检索方法，对规则模型中的属性进行统计分析，利用信息检索标准指标计算各属性对预测的贡献度，向用户提供可理解的风险因素影响信息。用户对比实验表明，该方法生成的输出在可解释性和实用性方面均优于原始可解释AI模型的输出。
原文摘要 · Abstract (English)
The lack of transparency of data-driven Artificial Intelligence techniques limits their interpretability and acceptance into healthcare decision-making processes. We propose an attribution-based approach to improve the interpretability of Explainable AI-based predictions in the specific context of arm lymphedema's risk assessment after lymph nodal radiotherapy in breast cancer. The proposed method performs a statistical analysis of the attributes in the rule-based prediction model using standard metrics from Information Retrieval techniques. This analysis computes the relevance of each attribute to the prediction and provides users with interpretable information about the impact of risk factors. The results of a user study that compared the output generated by the proposed approach with the raw output of the Explainable AI model suggested higher levels of interpretability and usefulness in the context of predicting lymphedema risk.
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