26 citations · 38 across the 7 of their papers we have counts for
8 papers
LayerNorm Induces Recency Bias in Transformer Decoders
Junu Kim, Xiao Liu, Zhenghao Lin +3
Causal self-attention provides positional information to Transformer decoders. Prior work has shown that stacks of causal self-attention layers alone induce a positional bias in at…
Enhancing LLMs' Clinical Reasoning with Real-World Data from a Nationwide Sepsis Registry
Junu Kim, Chaeeun Shim, Sungjin Park +16
Although large language models (LLMs) have demonstrated impressive reasoning capabilities across general domains, their effectiveness in real-world clinical practice remains limite…
Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records
Daeun Kyung, Junu Kim, Tackeun Kim +1
Chest X-ray (CXR) is an important diagnostic tool widely used in hospitals to assess patient conditions and monitor changes over time. Recently, generative models, specifically dif…
Federated Learning for Heterogeneous Electronic Health Record Systems with Cost Effective Participant Selection
Jiyoun Kim, Junu Kim, Kyunghoon Hur +1
The increasing volume of electronic health records (EHRs) presents the opportunity to improve the accuracy and robustness of models in clinical prediction tasks. Unlike traditional…
General-Purpose Retrieval-Enhanced Medical Prediction Model Using Near-Infinite History
Junu Kim, Chaeeun Shim, Bosco Seong Kyu Yang +4
Machine learning (ML) has recently shown promising results in medical predictions using electronic health records (EHRs). However, since ML models typically have a limited capabili…
Publicly Shareable Clinical Large Language Model Built on Synthetic Clinical Notes
Sunjun Kweon, Junu Kim, Jiyoun Kim +12
The development of large language models tailored for handling patients' clinical notes is often hindered by the limited accessibility and usability of these notes due to strict pr…
Universal EHR Federated Learning Framework
Junu Kim, Kyunghoon Hur, Seongjun Yang +1
Federated learning (FL) is the most practical multi-source learning method for electronic healthcare records (EHR). Despite its guarantee of privacy protection, the wide applicatio…
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
Kyunghoon Hur, Jungwoo Oh, Junu Kim +7
Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a par…