Predicting Lung Cancer Patient Prognosis with Large Language Models
This work addresses prognosis prediction for lung cancer patients, offering a potential tool when data is limited, but it is incremental as it applies existing LLMs to a new medical domain.
The study evaluated GPT-4o mini and GPT-3.5 for predicting lung cancer patient prognosis, finding that these large language models achieved competitive or superior performance compared to logistic regression models on survival and post-operative complication tasks without using additional patient data.
Prognosis prediction is crucial for determining optimal treatment plans for lung cancer patients. Traditionally, such predictions relied on models developed from retrospective patient data. Recently, large language models (LLMs) have gained attention for their ability to process and generate text based on extensive learned knowledge. In this study, we evaluate the potential of GPT-4o mini and GPT-3.5 in predicting the prognosis of lung cancer patients. We collected two prognosis datasets, i.e., survival and post-operative complication datasets, and designed multiple tasks to assess the models' performance comprehensively. Logistic regression models were also developed as baselines for comparison. The experimental results demonstrate that LLMs can achieve competitive, and in some tasks superior, performance in lung cancer prognosis prediction compared to data-driven logistic regression models despite not using additional patient data. These findings suggest that LLMs can be effective tools for prognosis prediction in lung cancer, particularly when patient data is limited or unavailable.