@inproceedings{wu-etal-2021-counterfactual,
title = "Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network",
author = "Wu, Haoran and
Chen, Wei and
Xu, Shuang and
Xu, Bo",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.156",
doi = "10.18653/v1/2021.naacl-main.156",
pages = "1942--1955",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network
%A Wu, Haoran
%A Chen, Wei
%A Xu, Shuang
%A Xu, Bo
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F wu-etal-2021-counterfactual
%X 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.
%R 10.18653/v1/2021.naacl-main.156
%U https://aclanthology.org/2021.naacl-main.156
%U https://doi.org/10.18653/v1/2021.naacl-main.156
%P 1942-1955
Markdown (Informal)
[Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network](https://aclanthology.org/2021.naacl-main.156) (Wu et al., NAACL 2021)
ACL