Search Results for author:
Found 3 papers, 0 papers with code
Beyond Self-Consistency: Ensemble Reasoning Boosts Consistency and Accuracy of LLMs in Cancer Staging
Using an open access clinical large language model to determine the pathologic cancer stage from real-world pathology reports, we show that the ensemble reasoning approach is able to improve both the consistency and performance of the LLM in determining cancer stage, thereby demonstrating the potential to use these models in clinical or other domains where reliability and trustworthiness are critical.
An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion
Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government.
Classifying Cancer Stage with Open-Source Clinical Large Language Models
Cancer stage classification is important for making treatment and care management plans for oncology patients.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
