Hyeonji Hwang

Gyubok Lee, Hyeonji Hwang, Seongsu Bae et al.

We present a new text-to-SQL dataset for electronic health records (EHRs). The utterances were collected from 222 hospital staff members, including physicians, nurses, and insurance review and health records teams. To construct the QA dataset on structured EHR data, we conducted a poll at a university hospital and used the responses to create seed questions. We then manually linked these questions to two open-source EHR databases, MIMIC-III and eICU, and included various time expressions and held-out unanswerable questions in the dataset, which were also collected from the poll. Our dataset poses a unique set of challenges: the model needs to 1) generate SQL queries that reflect a wide range of needs in the hospital, including simple retrieval and complex operations such as calculating survival rate, 2) understand various time expressions to answer time-sensitive questions in healthcare, and 3) distinguish whether a given question is answerable or unanswerable. We believe our dataset, EHRSQL, can serve as a practical benchmark for developing and assessing QA models on structured EHR data and take a step further towards bridging the gap between text-to-SQL research and its real-life deployment in healthcare. EHRSQL is available at https://github.com/glee4810/EHRSQL.

Seongjun Yang, Hyeonji Hwang, Daeyoung Kim et al.

Federated learning (FL) is an active area of research. One of the most suitable areas for adopting FL is the medical domain, where patient privacy must be respected. Previous research, however, does not provide a practical guide to applying FL in the medical domain. We propose empirical benchmarks and experimental settings for three representative medical datasets with different modalities: longitudinal electronic health records, skin cancer images, and electrocardiogram signals. The likely users of FL such as medical institutions and IT companies can take these benchmarks as guides for adopting FL and minimize their trial and error. For each dataset, each client data is from a different source to preserve real-world heterogeneity. We evaluate six FL algorithms designed for addressing data heterogeneity among clients, and a hybrid algorithm combining the strengths of two representative FL algorithms. Based on experiment results from three modalities, we discover that simple FL algorithms tend to outperform more sophisticated ones, while the hybrid algorithm consistently shows good, if not the best performance. We also find that a frequent global model update leads to better performance under a fixed training iteration budget. As the number of participating clients increases, higher cost is incurred due to increased IT administrators and GPUs, but the performance consistently increases. We expect future users will refer to these empirical benchmarks to design the FL experiments in the medical domain considering their clinical tasks and obtain stronger performance with lower costs.

Jiho Kim, Woosog Chay, Hyeonji Hwang et al.

Recent advancements in Large Language Models (LLMs) have significantly enhanced conversational agents, making them applicable to various fields (e.g., education, entertainment). Despite their progress, the evaluation of the agents often overlooks the complexities of real-world conversations, such as multi-party dialogues and extended contextual dependencies. To bridge this gap, we introduce DialSim, a dialogue simulation-based evaluation framework. In DialSim, an agent assumes the role of a character in a scripted conversation and is evaluated on their ability to answer spontaneous questions using only the dialogue history, while recognizing when they lack sufficient information. To support this framework, we introduce LongDialQA, a new QA dataset constructed from long-running TV shows, comprising over 1,300 dialogue sessions, each paired with more than 1,000 carefully curated questions, totaling over 352,000 tokens. To minimize reliance on prior knowledge, all character names are anonymized or swapped. Our evaluation of state-of-the-art LLM-based conversational agents using DialSim reveals that even models with large context windows or RAG capabilities struggle to maintain accurate comprehension over long-term, multi-party interactions-underscoring the need for more realistic and challenging benchmarks in conversational AI.