MILD Bot: Multidisciplinary Childhood Cancer Survivor Question-Answering Bot (2024)

Abstract: This study introduces a Multidisciplinary chILDhood cancer survivor question-answering (MILD) bot designed to support childhood cancer survivors facing diverse challenges in their survivorship journey. In South Korea, a shortage of experts equipped to address these unique concerns comprehensively leaves survivors with limited access to reliable information. To bridge this gap, our MILD bot employs a dual-component model featuring an intent classifier and a semantic textual similarity model. The intent classifier first analyzes the user’s query to identify the underlying intent and match it with the most suitable expert who can provide advice. Then, the semantic textual similarity model identifies questions in a predefined dataset that closely align with the user’s query, ensuring the delivery of relevant responses. This proposed framework shows significant promise in offering timely, accurate, and high-quality information, effectively addressing a critical need for support among childhood cancer survivors.

## introduction in recent decades, there have seen remarkable advancements in pediatric cancer survival rates, encompassing cancers diagnosed in children and adolescents aged 0 to 19 years @xcite . today, nearly 80% of these children achieve longterm survivor @xcite . similarly, † corresponding author south korea has achieved an impressive average 5year survival rate @xcite @xcite @xcite for childhood cancer, reaching 86.5% @xcite . however, the growing number of survivors highlights the need to address their complex psychological and social needs @xcite . south korea still lacks a comprehensive system for providing necessary psychosocial support @xcite , in contrast to the more developed systems in the united states @xcite . furthermore, survivors often face challenges in accessing support services due to fears of disclosing their medical history and associated stigma, complicating their adjustment and well-being @xcite @xcite . the covid-19 pandemic has accelerated the adoption of digital health technologies, including conversational agents, in oncological care @xcite . these technologies are now crucial for cancer screening, patient education, symptom monitoring, and psychological support. notable examples include chemofreebot, which aids breast cancer patients in self-care @xcite , and vivibot, which helps young adult cancer survivors manage anxiety @xcite . despite their benefits, @xcite found that the use of conversational agents in cancer care remains limited, especially for childhood cancer survivors. to address the critical gap in support for childhood cancer survivors, we propose a multidisciplinary question-answering (qa) bot named the multidisciplinary childhood cancer survivor question-answering bot (mild). this bot offers a stigma-free platform for accessing information, resources, and emotional support. using data from 860 academic articles, 2 publications, 18,565 news articles, 23 credible social media platforms, and 25 youtube videos, we utilized openai's gpt-4 turbo model to generate precise responses. our mild bot framework includes an intent classifier to understand user queries and a semantic textual similarity (sts) model to fetch relevant answers from a preestablished database. the main contributions of this paper are: ## background the core mechanism of the mild bot for childhood cancer survivors is an sts model, which retrieves relevant answers. to enhance its performance, we investigated several korean sts datasets for fine-tuning, as summarized in ## datasets for childhood cancer survivors in our study, we utilized various datasets to develop the mild bot model, including both the benchmark dataset discussed in section 2 and new datasets created specifically for our research. ## the proposed scheme the mild bot consists of two main components. first, an intent classifier identifies the intention behind the survivors' queries. then, an sts model matches the query with the most relevant question from the expert qa and peer survivor qa datasets. based on survivors' preferences, the system retrieves the most appropriate response. figure 2 illustrates the overall architecture of our mild bot model. ## experiment we conducted experiments to enhance our mild bot's performance, evaluating different korean pretrained language models for optimal training efficiency. first, we chose kcbert (lee, 2020) for its robust handling of typological errors, given its pretraining on a large online corpus. second, we selected km-bert @xcite , pretrained on a korean medical corpus, to better understand medical terminology. finally, we chose klue bert @xcite for its superior performance among korean bert models. we fine-tuned the intent classifier using these models to assess their impact on performance. our evaluation included domain-adaptive training and an ablation study to refine its effectiveness. additionally, we conducted a human evaluation with 14 childhood cancer survivors who interacted with our mild bot. ## conclusion we developed the mild bot, a multidisciplinary question-answering bot specifically for childhood cancer survivors. based on survey findings, we prioritized providing accurate information with empathetic tones. to build the bot, we gathered diverse data from academic articles, social media, youtube, news sources, and peer survivors' utterances. using these datasets, we performed domain adaptive training on the klue bert model to enhance mild bot's understanding of relevant information. the mild bot features an intent classifier to identify query intentions and an sts model to retrieve and provide the most appropriate answers from a predefined database, ensuring precise information tailored to the needs of childhood cancer survivors.