10 citations · 18 across the 17 of their papers we have counts for
5 papers · 1 filter
FEET: A Framework for Evaluating Embedding Techniques
Simon A. Lee, John Lee, Jeffrey N. Chiang
In this study, we introduce FEET, a standardized protocol designed to guide the development and benchmarking of foundation models. While numerous benchmark datasets exist for evalu…
Text Serialization and Their Relationship with the Conventional Paradigms of Tabular Machine Learning
Kyoka Ono, Simon A. Lee
Recent research has explored how Language Models (LMs) can be used for feature representation and prediction in tabular machine learning tasks. This involves employing text seriali…
Enhancing Antibiotic Stewardship using a Natural Language Approach for Better Feature Representation
Simon A. Lee, Trevor Brokowski, Jeffrey N. Chiang
The rapid emergence of antibiotic-resistant bacteria is recognized as a global healthcare crisis, undermining the efficacy of life-saving antibiotics. This crisis is driven by the…
Can Large Language Models abstract Medical Coded Language?
Simon A. Lee, Timothy Lindsey
Large Language Models (LLMs) have become a pivotal research area, potentially making beneficial contributions in fields like healthcare where they can streamline automated billing…
Emergency Department Decision Support using Clinical Pseudo-notes
Simon A. Lee, Sujay Jain, Alex Chen +4
In this work, we introduce the Multiple Embedding Model for EHR (MEME), an approach that serializes multimodal EHR tabular data into text using pseudo-notes, mimicking clinical tex…