A Speaker-Aware Co-Attention Framework for Medical Dialogue Information Extraction (2022)

Abstract: With the development of medical digitization, the extraction and structuring of Electronic Medical Records (EMRs) have become challenging but fundamental tasks. How to accurately and automatically extract structured information from medical dialogues is especially difficult because the information needs to be inferred from complex interactions between the doctor and the patient. To this end, in this paper, we propose a speaker-aware co-attention framework for medical dialogue information extraction. To better utilize the pre-trained language representation model to perceive the semantics of the utterance and the candidate item, we develop a speaker-aware dialogue encoder with multi-task learning, which considers the speaker’s identity into account. To deal with complex interactions between different utterances and the correlations between utterances and candidate items, we propose a co-attention fusion network to aggregate the utterance information. We evaluate our framework on the public medical dialogue extraction datasets to demonstrate the superiority of our method, which can outperform the state-of-the-art methods by a large margin. Codes will be publicly available upon acceptance.

## introduction in the past decade, the collection and usage of electronic medical records (emrs) have been proved as one of the most important applications in the process of medical digitization. however, the recording and writing of the emrs may bring a significant burden to doctors. given the breakthrough advance of speech recognition technology, conversations between doctors and patients can be accurately recorded as text. however, such unstructured medical dialogue data cannot be easily utilized for medical research. how to automatically extract the structured information from these unstructured textual medical dialogue data is an essential step to accelerate medical digitization. compared with the general medical information extraction, the crucial challenge of the medical dialogue extraction is that it has to take the speaker's identity and utterance interactions into consideration. in conventional information extraction, a relation can largely be inferred by a sentence or a paragraph. however, in the medical dialogue extraction task, the candidate item and status information need to be detected and then verified by the conversations between the doctor and the patient. an example of a patient-doctor dialogue and the corresponding annotated labels is shown in figure 1 . for instance, the doctor asks the patient, "did you feel angina?", the patient responds, "no, i felt there is a sense of suppression in the chest.", the ground truth labels for correct extraction are (chest tightness: patient-positive), (angina: patientnegative). if only considering the utterance of the patient or the doctor alone, we cannot make correct information extraction. however, how to leverage the speaker's identity and utterance interactions information to facilitate medical information extraction is not well explored. @xcite describe a novel model that extracts the symptoms mentioned in clinical conversations along with their status. the annotation of their status does not consider the speaker's identity into account. @xcite make symptom recognition and symptom inference in medical dialogues, and propose a global attention mechanism to capture symptom-related information. @xcite develop a medical information extractor based on a simple deep matching module to take turn interaction into consideration. thus, all existing methods fail to take the speaker into consideration, and the simple utterance combination method such as just concatenating all utterances together with flat attention cannot grasp sufficient information among utterance interactions in the medical dialogue. to tackle the above challenge, we propose a speaker-aware co-attention framework for medical dialogue extraction (name safe for short). first, to better predict the status of the candidate item in the medical dialogue, we should both consider the contextual information from the dialogue and be aware of the identity of the speaker. for the annotated label (echocardiography: doctorpositive) in the dialogue shown in figure 1 , being aware of the identity (patient or doctor) of the speaker can help make a correct inference. second, we propose an utterance-based co-attention graph network to perceive complex correlations between different utterances. we summarize our contributions as follows: ## preliminaries in this section, we formally define the problem of medical dialogue extraction (mde). for a dialogue with n tokens and m utterances, it can be defined as @xmath0, u r 2 2 , • • • , u rm m ), where u r i i is the i-th utterance in the dialogue, r @xmath1, 1}, which indicates the speaker's identity (e.g. belongs to patient or doctor). the candidate item @xmath2, disease, surgery, etc.) which can be extracted from a dialogue d. for each candidate item i, we also need to identify its status @xmath3, patient-positive, patientunknown, doctor-positive, doctor-negative} which indicates whether the candidate item is confirmed or denied by doctors and patients. finally, we define the task of medical dialogue extraction as follows: given a medical dialogue @xmath4, candidate item @xmath5, the mde can be formulated to predict the label f : @xmath6, i.e. @xmath7, and y @xmath8. note that different from the task for relation extraction (re), the label space for the mde is very sparse, which causes it a more challenging problem. ## method we develop a three-stage pipeline system: (1) speaker-aware dialogue encoder module (sae), a module to turn the utterances in the medical dialogue and the candidate item into node feature representations, which also takes the speaker identity into account; (2) co-attention fusion module (caf), a module to involve the interactions between the utterances and the correlation between utterance and candidate item into consideration; and (3) inference module (im), a module to utilize the fusion representation for final dialogue information extraction. the full pipeline of our proposed medical dialogue extraction framework is illustrated in figure 2 . ## conclusion in this paper, we propose a speaker-aware coattention framework for medical dialogue information extraction. we design a speaker-aware dialogue encoder module, which considers the speaker's identity into account and can better utilize the pre-trained language model to capture the semantics of the utterance and the candidate item. moreover, we propose a co-attention fusion network to aggregate the utterance information, which tackles complex interactions between different utterances and the correlation between utterances and candidate items. the experiment results demonstrate the effectiveness of the proposed framework.