Knowledge Commons of Institute of Automation,CAS
Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network | |
Wu HR(吴浩然)1,2; Chen W(陈炜)1,2; Xu S(徐爽)1,2; Xu B(徐波)1,2 | |
2021-06 | |
会议名称 | 2021 Conference of the North American Chapter of the Association for Computational Linguistics |
会议日期 | June 6–11, 2021 |
会议地点 | Online |
出版地 | Online |
出版者 | Association for Computational Linguistics |
摘要 | Providing a reliable explanation for clinical diagnosis based on the Electronic Medical Record (EMR) is fundamental to the application of Artificial Intelligence in the medical field. Current methods mostly treat the EMR as a text sequence and provide explanations based on a precise medical knowledge base, which is disease-specific and difficult to obtain for experts in reality. Therefore, we propose a counterfactual multi-granularity graph supporting facts extraction (CMGE) method to extract supporting facts from irregular EMR itself without external knowledge bases in this paper. Specifically, we first structure the sequence of EMR into a hierarchical graph network and then obtain the causal relationship between multi-granularity features and diagnosis results through counterfactual intervention on the graph. Features having the strongest causal connection with the results provide interpretive support for the diagnosis. Experimental results on real Chinese EMR of the lymphedema demonstrate that our method can diagnose four types of EMR correctly, and can provide accurate supporting facts for the results. More importantly, the results on different diseases demonstrate the robustness of our approach, which represents the potential application in the medical field. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 自然语言处理 |
国重实验室规划方向分类 | 语音语言处理 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/52134 |
专题 | 复杂系统认知与决策实验室 |
通讯作者 | Wu HR(吴浩然) |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Wu HR,Chen W,Xu S,et al. Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network[C]. Online:Association for Computational Linguistics,2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2021.naacl-main.156.(1394KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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