Language Models are Few-shot Learners for Prognostic Prediction

Understand

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.