Model Distillation for Faithful Explanations of Medical Code Predictions

Explore knowledge distillation techniques for generating faithful and plausible explanations in machine learning models, particularly in clinical medicine and high-risk settings. Delve into Isabel Cachola's research from Johns Hopkins University's Center for Language & Speech Processing, focusing on improving interpretability of models with excellent predictive performance. Learn how this approach can support integrated human-machine decision-making and increase trust in model predictions among domain experts. Examine the application of these techniques to medical code predictions, based on the paper presented at the BioNLP 2022 workshop.