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Found 5 papers, 0 papers with code
Explainable AI for Fair Sepsis Mortality Predictive Model
By focusing on the predictive modeling of sepsis-related mortality, we propose a method that learns a performance-optimized predictive model and then employs the transfer learning process to produce a model with better fairness.
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.
Constructing Cross-lingual Consumer Health Vocabulary with Word-Embedding from Comparable User Generated Content
The experimental results demonstrate that our framework outperforms the other two large language models in identifying CHV across languages.
