Counterfactual Supporting Facts Extraction for Explainable Medical Record Based Diagnosis with Graph Network (2021)

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.

## introduction electronic medical record (emr) based diagnosis has attracted extensive attention due to its comprehensive historical information and clinical descriptions with the development of natural language processing and medical informatics @xcite @xcite @xcite . the application of deep learning in medicine requires adequate medical explanations for the result. specific to the diagnosis of emr, the model needs to provide the text description supporting the diagnosis results. figure 1 : an example of emr. we consolidated the various parts of the emr into a single document as input and our goal is to extract supporting facts at the granularity of the clause. as shown in figure 1 , an irregular emr is a document of disease-related information, including symptoms, history of the disease, preliminary examination results, and so on, which is disordered and sparse with meaningless noisy text. existing methods provide explanation through medical entities @xcite ), text spans @xcite and the weights of external knowledge @xcite . the entity is critical to the diagnosis @xcite , but for the medical explanation, it cannot provide specific information of symptoms (such as positive or negative). and the form of the span is too fragmented and lacks readability. therefore, the clause as a more informative and readable representation is needed to be combined above the level of entities. most of the previous methods provide reliable explanations for diagnosis by calculating the similarity with an external medical knowledge base (icd foot_1 and ccs foot_2 ) @xcite . kame @xcite uses the weights of the nodes in the introduced knowledge graph to provide explanations. depending on the hierarchical relations in the database, gman @xcite builds a disease hierarchy graph and a causal graph to find critical entities. however, a trusted medical knowledge base requires a mass of expertise in different fields to build, and it may be incomplete or erroneous in practical clinical applications. so far, how to extract supporting facts from the emr itself without an external medical knowledge base is still a problem. counterfactual reasoning provides a link between what could have happened when inputs had been changed @xcite . doctors usually make a judgment based on several related symptoms during diagnosing a disease. in this regard we can consider a question: will a doctor make a misdiagnosis without one of the critical symptoms? the result is clear. in a counterfactual way, if we gradually weaken the features until the diagnosis changes dramatically, then this feature can be considered as a supporting fact. based on this consensus, we propose a counterfactual multi-granularity graph supporting facts extraction (cmge) method for the irregular emr in this paper. first, we model the emr as a hierarchical graph structure, which contains sentences, clauses, and entities. specifically, sentences are used to model the temporal relationship, clauses provide a complete descriptive explanation, and entities provide symptom support as others. on this basis, we use a graph attention network to aggregate all information from different granularities. then, we can do a counterfactual intervention to obtain the causal relation between feature and diagnosis. specifically, we train a learnable soft-mask matrix to mask the feature of nodes or edges in the graph while keeping the diagnosis unchanged, and the remaining features are the supporting facts of the diagnosis. counterfactual reasoning on the graph requires enhancing the medical features contained in the text of different granularity, so we use clustering labels 4 to cluster clauses and entities. the experimental results demonstrate the effectiveness of our method. the contributions of this paper are summarized as follows: figure 2 : this figure shows the hierarchical connection structure between multi-grained nodes. the black edges in the graph represent the tree structure connection between the four types of nodes in the emr. for the red edges, the left part shows the connection between the clause nodes and the graph aggregate nodes, and the right part shows the fully connected form between clause nodes. ## proposed method with l words, the task for us is to extract supporting facts that can be used to explain the diagnosis result without relying on external knowledge while performing diagnosis. the supporting facts can be entities or clauses of text. ## conclusion in this paper, we propose a counterfactual multigranularity graph supporting facts extraction (cmge) method for the irregular emr without an external medical knowledge base. based on this model, we can correctly diagnose lymphedema. the proposed counterfactual-based approach can discover the causal relationship between symptoms and diagnosis. the results of supporting fact extraction show that our method has strong robustness and can maintain accuracy in various diseases and even in categories with few data resources. in the future, we will introduce multi-modal into the model such as radiology images to discover more medical knowledge from emrs.