分析医疗编码证据数据集，提升自动编码系统可解释性。
The Anatomy of Evidence: An Investigation Into Explainable ICD Coding
- 基于MDACE数据集深入分析编码证据匹配度。
- 现有方法与真实证据重合度高，但存在明显失败案例。
- 为可解释医疗编码系统提供评估和改进建议。
自动医疗编码有望减轻文档与计费负担。透明性对医疗编码员和监管机构至关重要，可通过可解释性方法实现。然而，当前评估多局限于短文本和二分类场景，受限于标注数据稀缺。Cheng等（2023）提出的MDACE数据集提供了临床记录中编码证据的宝贵资源。本文深入分析该数据集，并从应用角度对现有可解释医疗编码系统进行合理性评估。研究发现，真实证据与编码描述在一定程度上一致；先进方法与真实证据高度重合。我们提出匹配度量标准，揭示成功与失败案例。基于结果，提出可解释医疗编码系统开发与评估的改进建议。
原文摘要 · Abstract (English)
Automatic medical coding has the potential to ease documentation and billing processes. For this task, transparency plays an important role for medical coders and regulatory bodies, which can be achieved using explainability methods. However, the evaluation of these approaches has been mostly limited to short text and binary settings due to a scarcity of annotated data. Recent efforts by Cheng et al. (2023) have introduced the MDACE dataset, which provides a valuable resource containing code evidence in clinical records. In this work, we conduct an in-depth analysis of the MDACE dataset and perform plausibility evaluation of current explainable medical coding systems from an applied perspective. With this, we contribute to a deeper understanding of automatic medical coding and evidence extraction. Our findings reveal that ground truth evidence aligns with code descriptions to a certain degree. An investigation into state-of-the-art approaches shows a high overlap with ground truth evidence. We propose match measures and highlight success and failure cases. Based on our findings, we provide recommendations for developing and evaluating explainable medical coding systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。