Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents

Thu, 01 Jan 2026 00:00:00 +0000

This study investigates whether fine-tuning open-weight large language models (LLMs) improves cancer survival prediction from oncology consultation notes compared to zero-shot approaches. Using Meta’s Llama models on 59,800 patient records from BC Cancer, fine-tuning consistently improved performance over zero-shot inference, but did not outperform smaller task-specific NLP models. The findings suggest both approaches merit continued investigation, with the best choice depending on clinical context and practical constraints such as hardware, privacy, and deployment feasibility.

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing

Mon, 27 Feb 2023 00:00:00 +0000

This study demonstrates that natural language processing applied to initial oncology consultation documents can effectively predict patient survival outcomes across multiple cancer types. The models perform comparably with or better than previous prediction models while using readily available clinical data.

Oncology | Nunez Lab for Computational Mental Health & Cancer Care

Investigating fine-tuning versus zero-shot learning for general large language models when predicting cancer survival from initial oncology consultation documents

Predicting the Survival of Patients With Cancer From Their Initial Oncology Consultation Document Using Natural Language Processing