让肺部X光报告分类结果可解释，通过查询关键事实来诊断。
IP-CRR: Information Pursuit for Interpretable Classification of Chest Radiology Reports
- 基于信息追求框架，动态选择最能说明病情的关键事实进行验证。
- 在MIMIC-CXR数据集上达到91.3%准确率，且解释过程清晰可追溯。
- 适合需要可解释性医疗AI的临床场景，如医生辅助诊断系统。
开发基于AI的放射科报告分析方法有望显著提升医学诊断水平，涵盖诊断准确性、效率提升和工作量减轻。然而，当前AI方法缺乏可解释性，限制了其在临床环境中的应用。本文提出一种可解释性设计的胸部放射科报告分类框架：首先从大量报告中提取一组代表性事实；对于新报告，通过查询一小部分代表性事实是否被报告所蕴含，并基于选中的查询-答案对预测疾病。预测的解释即为所选查询与答案的集合。我们采用信息追求框架选择最具信息量的查询，使用自然语言推理模型判断事实是否被报告蕴含，并用分类器完成诊断。在MIMIC-CXR数据集上的实验表明该方法有效，展现出增强医疗AI可信度与可用性的潜力。
原文摘要 · Abstract (English)
The development of AI-based methods to analyze radiology reports could lead to significant advances in medical diagnosis, from improving diagnostic accuracy to enhancing efficiency and reducing workload. However, the lack of interpretability of AI-based methods could hinder their adoption in clinical settings. In this paper, we propose an interpretable-by-design framework for classifying chest radiology reports. First, we extract a set of representative facts from a large set of reports. Then, given a new report, we query whether a small subset of the representative facts is entailed by the report, and predict a diagnosis based on the selected subset of query-answer pairs. The explanation for a prediction is, by construction, the set of selected queries and answers. We use the Information Pursuit framework to select the most informative queries, a natural language inference model to determine if a fact is entailed by the report, and a classifier to predict the disease. Experiments on the MIMIC-CXR dataset demonstrate the effectiveness of the proposed method, highlighting its potential to enhance trust and usability in medical AI.
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