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}.

翻译：临床预测是医疗行业中的一项关键任务。然而，近年来基于大型语言模型构建的Transformer架构的成功尚未延伸至该领域。本研究探索了利用真实世界患者的临床数据和分子特征，将Transformer及语言模型应用于免疫治疗的预后预测。本文探究了与传统机器学习方法相比，Transformer在改善临床预测方面的潜力，并解决了罕见疾病预测中的小样本学习挑战。研究在多癌种预后预测中基准测试了基线模型与语言模型的有效性，并考察了不同预训练语言模型在小样本场景下的影响。结果表明，准确率得到显著提升，并凸显了自然语言处理在临床研究中促进不同疾病早期检测与干预的潜力。匿名代码详见\url{https://anonymous.4open.science/r/table2text-88ED}。