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. Anonymous codes are available at \url{https://anonymous.4open.science/r/table2text-88ED}.

研究使用transformers和语言模型在预测免疫疗法的预后预测中的潜力，改善临床预测。通过在多种癌症类型上跨比较，该研究发现使用NLP在临床研究中可以显著提高准确性，有助于不同疾病的早期检测和干预。

语言模型是预测性预测的几乎零-shot学习器