Language Models are Few-shot Learners for Prognostic Prediction
This work addresses the problem of few-shot learning in clinical prediction for rare diseases, offering potential improvements in early detection and intervention, though it is incremental in applying existing NLP methods to a new domain.
The paper tackled prognostic prediction for immunotherapy using clinical and molecular data, showing that transformers and language models significantly improve accuracy compared to conventional methods in few-shot learning scenarios for rare diseases.
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.