10 papers
TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs
Hyeongwon Jang, Gyouk Chu, Changhun Kim +3
Clinical early warning systems built on electronic health records, in which clinical observations are recorded as irregularly sampled medical time series (ISMTS), must deliver both…
Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring
Changhun Kim, Yechan Mun, Hyeongwon Jang +3
Explaining online time series monitoring models is crucial across sensitive domains such as healthcare and finance, where temporal and contextual prediction dynamics underpin criti…
ReviewScore: Misinformed Peer Review Detection with Large Language Models
Hyun Ryu, Doohyuk Jang, Hyemin S. Lee +16
Peer review serves as a backbone of academic research, but in most AI conferences, the review quality is degrading as the number of submissions explodes. To reliably detect low-qua…
Structure-Aware Set Transformers: Temporal and Variable-Type Attention Biases for Asynchronous Clinical Time Series
Joohyung Lee, Kwanhyung Lee, Changhun Kim +1
Electronic health records (EHR) are irregular, asynchronous multivariate time series. As time-series foundation models increasingly tokenize events rather than discretizing time, t…
Soft Equivariance Regularization for Invariant Self-Supervised Learning
Joohyung Lee, Changhun Kim, Hyunsu Kim +2
Self-supervised learning (SSL) typically learns representations invariant to semantic-preserving augmentations. While effective for recognition, enforcing strong invariance can sup…
DeltaSHAP: Explaining Prediction Evolutions in Online Patient Monitoring with Shapley Values
Changhun Kim, Yechan Mun, Sangchul Hahn +1
This study proposes DeltaSHAP, a novel explainable artificial intelligence (XAI) algorithm specifically designed for online patient monitoring systems. In clinical environments, di…