목록LLM (2)
Seung-MinJi
[Review] Leveraging Large Language Models to Enhance Machine Learning Interpretability and Predictive Performance: A Case Study on Emergency Department Returns for Mental Health Patients
0. AbstractImportance: Emergency department (ED) returns for mental health conditions represent a significant healthcare burden, with about 24 - 27% of mental health patients returning within 30 days. Traditional machine learning models for predicting these returns often lack interpretability for clinical implementation. Objective: To evaluate whether integrating large language models (LLMs) wit..
[Review] Using a fine-tuned large language modelfor symptom-based depression evaluation
0. AbstractRecent advances in artificial intelligence, particularly large language models (LLMs), show promise for mental health applications, including the automated detection of depressive symptoms from natural language. We fine-tuned a German BERT-based LLM to predict individual Montgomery-Åsberg Depression Rating Scale (MADRS) scores using a regression approach across different symptom items..