Abstract
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.- Anthology ID:
- 2021.naacl-main.156
- Volume:
- Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1942–1955
- Language:
- URL:
- https://aclanthology.org/2021.naacl-main.156
- DOI:
- 10.18653/v1/2021.naacl-main.156
- Cite (ACL):
- Haoran Wu, Wei Chen, Shuang Xu, and Bo Xu. 2021. Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1942–1955, Online. Association for Computational Linguistics.
- Cite (Informal):
- Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (Wu et al., NAACL 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.naacl-main.156.pdf
- Code
- ckre/cmge