摘要

Cancer patients frequently experience depression due to the severity of the disease and its poor prognosis. To improve the identification of depression, researchers recently developed classifiers using logistic regression, support vector machines (SVMs), and 2 Bidirectional Encoder Representations from Transformers (BERT) models—one original and one further pretrained on Reddit data—fine-tuned on 6600 patient messages from a cancer center.1 While the authors rightly highlight “the potential of BERT and RedditBERT in identifying depression concerns in messages from cancer patients,” we believe their models may not have been fully optimized. In this paper, we explain why this is the case, propose solutions through hyperparameter tuning, and suggest further improvements using alternative transformer models within the BERT family. We hope our insights will help researchers and practitioners enhance patient care and improve quality of life.

引用

Yu Wang,Xin Ye,Huiping Luo & Wei Feng.(2025).Large language models for identifying depression concerns in cancer patients..Journal of the American Medical Informatics Association : JAMIA,
https://doi.org/10.1093/JAMIA/OCAF072.