5 papers · 1 filter
Lessons Learned on Information Retrieval in Electronic Health Records: A Comparison of Embedding Models and Pooling Strategies
Skatje Myers, Timothy A. Miller, Yanjun Gao +4
Objective: Applying large language models (LLMs) to the clinical domain is challenging due to the context-heavy nature of processing medical records. Retrieval-augmented generation…
When Raw Data Prevails: Are Large Language Model Embeddings Effective in Numerical Data Representation for Medical Machine Learning Applications?
Yanjun Gao, Skatje Myers, Shan Chen +5
The introduction of Large Language Models (LLMs) has advanced data representation and analysis, bringing significant progress in their use for medical questions and answering. Desp…
Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data
Shan Chen, Jack Gallifant, Marco Guevara +5
Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synth…
The impact of responding to patient messages with large language model assistance
Shan Chen, Marco Guevara, Shalini Moningi +12
Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (A…
Leveraging Medical Knowledge Graphs Into Large Language Models for Diagnosis Prediction: Design and Application Study
Yanjun Gao, Ruizhe Li, Emma Croxford +6
Electronic Health Records (EHRs) and routine documentation practices play a vital role in patients' daily care, providing a holistic record of health, diagnoses, and treatment. How…