Dialogue Medical Information Extraction with Medical-Item Graph and Dialogue-Status Enriched Representation (2023)

Abstract: The multi-turn doctor-patient dialogue includes rich medical knowledge, like the symptoms of the patient, the diagnosis and medication suggested by the doctor. If mined and represented properly, such medical knowledge can benefit a large range of clinical applications, including diagnosis assistance and medication recommendation. To derive structured knowledge from free text dialogues, we target a critical task: the Dialogue Medical Information Extraction (DMIE). DMIE aims to detect pre-defined clinical meaningful medical items (symptoms, surgery, etc.) as well as their statuses (positive, negative, etc.) from the dialogue. Existing approaches mainly formulate DMIE as a multi-label classification problem and ignore the relationships among medical items and statuses. Different from previous approaches, we propose a heterogeneous graph to model the relationship between items. We further propose two consecutive attention based modules to enrich the item representation with the dialogue and status. In this manner, we are able to model the relationships among medical items and statuses in the DMIE task. Experimental results on the public benchmark data set show that the proposed model outperforms previous works and achieves the state-of-the-art performance.

## introduction the digitization of medical systems over the past decade has accumulated massive medical data. among various medical data, the multi-turn doctorpatient dialogue contains rich medical knowledge, like the suggested medical test according to patient symptom descriptions, the recommended medication according to doctor's diagnosis results, etc. if such medical knowledge can be properly extracted and represented, it can enable the development of a large range of clinical applications, like disease doctor-patient conversation patient: 医生你好，频发室早是什么原因，严重吗？ (hello, doctor. what is the reason for the frequent ventricular premature beats? is it serious?) doctor: 我将为您提供就诊指导。频发室早是心脏传导系统出了问题。 严重不严重要根据具体情况才能定。做了心电图了吗？ (i will provide you with online guidance for medical treatment. frequent ventricular premature beats are problems with the conduction system of the heart. whether it is serious or not can be determined according to the specific situation. have you got an electrocardiogram?) patient: 血压低就会有危险吗？还没做心电图。治疗是怎样治疗的？ (is there any danger from low blood pressure? i haven't done an electrocardiogram. how is it treated?) doctor: 血压偏低不一定有危险。最好做一下24小时动态心电图，了 解一下全天室早早搏有多少，需要的话可以射频消融术治疗。 (low blood pressure is not necessarily dangerous. it is better to do a 24-hour dynamic electrocardiogram to find out how many premature ventricular beats throughout the day. radiofrequency ablation can be used for treatment if necessary.) patient: 好的，谢谢医生。 (ok, thank you doctor.) extracted labels category item status label 1 symptom frequent ventricular premature beats patient-pos label 2 symptom hypotension unknown label 3 surgery radiofrequency ablation doctor-pos label 4 test electrocardiogram patient-neg label 5 test electrocardiogram doctor-pos diagnosis assistance and medication recommendation. to this end, we target a critical task: the dialogue medical information extraction (dmie). given a multi-turn doctor-patient dialogue, the dmie task aims to label this dialogue with predefined medical items and their corresponding statuses. we use an example from the mieacl data set foot_0 @xcite to illustrate the dmie task. as shown in figure 1 , this dialogue receives five labels in total. each label consists of three sub-components: category, item and status, and their combination provides detailed medical descriptions. as shown in most existing approaches either focus on recognizing a subset of items or directly formulate dmie as a classification task. among existing dmie works, lin et al. @xcite and du et al. @xcite only exploited the symptom items. they first recognized symptom terms from dialogues and then classified their corresponding statuses; zhang et al. @xcite proposed a deep neural matching model which is actually a multi-label text classification (mltc) model. each combination of item and status refers to an independent class label. recently, li et al. @xcite modeled the extraction as a generation process, and developed a multi-granularity transformer which can effectively capture the interaction between roleenhanced crossturns and integrate representations of mixed granularity. xia et al. @xcite proposed a speaker-aware co-attention framework to address the intricate interactions among different utterances and the correlations between utterances and candidate items. all aforementioned methods disregard the relations among items as well as the relations between items and statuses. in this paper, we propose a novel approach to model 1) the relations among items and 2) the relations between items and statuses in the dmie task. firstly, we adopt a pre-trained language model to learn representations for dialogues, items and statuses respectively; secondly, we introduce • we propose a novel model for the dmie, which introduces the interactions among medical items, incorporates the doctor-patient dialogue information to item representations and explores the item-status relationships. they also presented a co-attention fusion network to combine the information from different utterances and handle the intricate interactions among them, as well as the correlations between the utterances and candidate items. existing dmie approaches treat each medical item independently. as a result, the correlations among medical items are ignored in modeling. different from the above works, we employ a heterogeneous graph to directly model the correlations between medical items. furthermore, we introduce an item updating with dialogue-aware attention module to infer implicit mention of medical items from dialogues. ## conclusions we introduce a heterogeneous item graph to model item correlations, as well as two attention based modules to learn a dialogue-status enriched joint representation for dialogue medical information extraction (dmie). instead of formulating dmie as an ordinary multi-label text classification problem, we consider the item-status relationship and model this relationship explicitly. extensive experiments demonstrated that each component of our proposed model could bring performance gains to the baseline model, and that their combination further improved the result and achieved the stateof-the-art performance on the mieacl benchmark. we also evaluate the performance of gpt-3.5-turbo and gpt-4. in future, we will attempt to include textual names of items and status in modeling.