Context Aggregation with Topic-focused Summarization for Personalized Medical Dialogue Generation (2024)

Abstract: In the realm of dialogue systems, generated responses often lack personalization. This is particularly true in the medical domain, where research is limited by scarce available domain-specific data and the complexities of modeling medical context and persona information. In this work, we investigate the potential of harnessing large language models for personalized medical dialogue generation. In particular, to better aggregate the long conversational context, we adopt topic-focused summarization to distill core information from the dialogue history, and use such information to guide the conversation flow and generated content. Drawing inspiration from real-world telehealth conversations, we outline a comprehensive pipeline encompassing data processing, profile construction, and domain adaptation. This work not only highlights our technical approach but also shares distilled insights from the data preparation and model construction phases.

## introduction medical dialogue systems hold significant potential for improving the efficiency of clinical workflows @xcite . as a specialized form of task-oriented dialogue, medical dialogue typically involves the completion of multiple tasks, including diagnosis, question answering, and consultation @xcite @xcite . there has been significant progress in this research field of the dialogue system in past years with the development of contextualized representation learning and neural language generation @xcite . however, the general-purpose conversational interactive systems are proven to be inadequate, as they cannot adapt their responses to the unique medical histories and the diverse user preferences and personalities @xcite . personalized dialogue systems, tailored to the specific needs and characteristics of different users, can potentially bridge this gap @xcite . by leveraging patient profiles, such as medical records, demographic information, and previous interactions, the personalized systems can facilitate more nuanced, empathetic, and context-aware conversations. this level of personalization not only enhances patient engagement and satisfaction, but also has the potential to improve healthcare outcomes by fostering adherence to treatment plans and providing tailored health education. in this work, we conduct a case study on a clinical conversation scenario. because of the chronic nature of diabetes and its associated complications, it requires constant attention and regular follow-up operation @xcite . in practice, nurses schedule calls with patients to track their compliance status and health condition, provide general education, and customized coaching and lifestyle advice @xcite . to facilitate the communication process and deliver more efficient health management, the follow-up calls are organized according to a medical protocol and telecarers adjust the conversation topics based on the patient's lifestyle management status and medication records @xcite . this renders the follow-up call a representative use case for personalized dialogue generation. for instance, customized coaching is an effective patient education method @xcite , and its sub-topics are strongly correlated to the patient profile (as the example shown in figure 1 ). the challenges of developing a personalized medical dialogue system come from three fundamental aspects: the lack of domain-specific data @xcite ; the complexity of modeling medical context and persona information @xcite ; and how to extensively evaluate the system @xcite . moreover, due to the verbal nature of human spoken dialogues, the follow-up calls are often lengthy by covering various topics, which results in a low information density. the noisy long context also poses challenges for modeling and generation. we thus propose and adopt topic-focused summarization to distill and aggregate core information of the dialogue context, and use such information to guide the subsequent conversation flow and content generation. in practice, to bootstrap the data-driven approaches, we construct a sample set derived from human spoken conversations, and we leverage the advancements in large language models (llms) @xcite for developing the dialogue system, which have demonstrated their exceptional language understanding and generation capabilities in the medical domain @xcite . we add user profile information to produce personalized conversation, and improve the generation coherence based on topiclevel context aggregation. experiments show that our proposed method can substantially improve the generation quality, especially in the long context setting. this work not only highlights the technical approach but also shares distilled insights from the data preparation and model construction phases. personalized dialogue systems one-size-fits-all approaches to human-machine communication have shown limitations in accommodating the diverse needs, preferences, and contexts of individual users. by contrast, personalized dialogue systems @xcite offer the potential to transcend these limitations by tailoring interactions to unique characteristics and requirements, thus raising much research interest. in particular, improving the modeling of persona or user information is one of the key points, and there are different approaches proposed in previous studies, such as explicitly utilizing pre-defined persona attributes to generate conditional responses @xcite , constructing user embeddings to enhance personalized dialogue generation @xcite , and building implicit user information from dialogue history @xcite . ## personalized dialogue generation: data preparation & refinement in this work, we conduct a case study on personalized follow-up calls for diabetes patients. diabetes is a chronic metabolic disorder characterized by abnormal glucose regulation, and effective management of diabetes is essential to mitigate its associated complications and improve patients' overall quality of life @xcite . in practical use cases, the general-purpose messages may not adequately address the unique needs of individual patients. for example, customized coaching of physical activity should take into account factors such as the patient's age, comorbidities, lifestyle, and psychosocial aspects. by recognizing the heterogeneity of diabetes patients and offering tailored coaching interventions, it is useful for improving health management. ## context aggregation via topic-focused summarization due to the complexity and verbose nature of human spoken dialogues @xcite , and the necessity to cover multiple topics in clinical follow-up calls, nurse-to-patient conversations are often lengthy and thus characterized by lower information density than other document formats. for instance, in our transcribed calls, the maximum, median, and minimum utterance numbers are 1996, 221, and 21, respectively; the maximum, median, and minimum number of words are 16701, 1684, and 70, respectively. nearly 5% samples (at the 95% quantile) are comprised of more than 800 utterances (6000 words). this requires models to precisely capture the core information from the long dialogue context and poses challenges for dialogue systems in both modeling and generation. in this work, we propose and adopt topic-focused summarization, to distill and aggregate the salient pieces from a noisy dialogue context. the refined context is then leveraged to guide the subsequent generation, and improve relevance and coherence. more specifically, we leverage the large language models to generate dialogue summaries for each dialogue snippet about a certain topic, and concatenate them as the history context. we conduct the following steps to build samples for training the data-driven approach: ## conclusion in this work, we investigated the feasibility and effectiveness of leveraging language language models for personalized medical dialogue generation. we conducted a case study on healthcare followup calls for diabetes management. inspired by real-world conversations, we built a data preparation and refinement pipeline for spoken conversation processing, user profile construction, and proposed topic-focused summarization to distill and aggregate the historical context. to exploit the potential of llms, we applied efficient model training methods for domain adaptation. our experimental results showed that context aggregation via topic-focused summarization is beneficial for long-context modeling and coherent generation.