Clinical prediction is an essential task in the healthcare industry. However,
the recent success of transformers, on which large language models are built,
has not been extended to this domain. In this research, we explore the use of
transformers and language models in prognostic prediction for immunotherapy
using real-world patients' clinical data and molecular profiles. This paper
investigates the potential of transformers to improve clinical prediction
compared to conventional machine learning approaches and addresses the
challenge of few-shot learning in predicting rare disease areas. The study
benchmarks the efficacy of baselines and language models on prognostic
prediction across multiple cancer types and investigates the impact of
different pretrained language models under few-shot regimes. The results
demonstrate significant improvements in accuracy and highlight the potential of
NLP in clinical research to improve early detection and intervention for
different diseases.Comment: 7 pages, 5 figures, 5 table
