Model Distillation for Faithful Explanations of Medical Code Predictions

Zach Wood-Doughty, Isabel Cachola, Mark Dredze


Abstract
Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making. In clinical medicine and other high-risk settings, domain experts may be unwilling to trust model predictions without explanations. Work in explainable AI must balance competing objectives along two different axes: 1) Models should ideally be both accurate and simple. 2) Explanations must balance faithfulness to the model’s decision-making with their plausibility to a domain expert. We propose to use knowledge distillation, or training a student model that mimics the behavior of a trained teacher model, as a technique to generate faithful and plausible explanations. We evaluate our approach on the task of assigning ICD codes to clinical notes to demonstrate that the student model is faithful to the teacher model’s behavior and produces quality natural language explanations.
Anthology ID:
2022.bionlp-1.41
Volume:
Proceedings of the 21st Workshop on Biomedical Language Processing
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
BioNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
412–425
Language:
URL:
https://aclanthology.org/2022.bionlp-1.41
DOI:
10.18653/v1/2022.bionlp-1.41
Bibkey:
Cite (ACL):
Zach Wood-Doughty, Isabel Cachola, and Mark Dredze. 2022. Model Distillation for Faithful Explanations of Medical Code Predictions. In Proceedings of the 21st Workshop on Biomedical Language Processing, pages 412–425, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Model Distillation for Faithful Explanations of Medical Code Predictions (Wood-Doughty et al., BioNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bionlp-1.41.pdf
Video:
 https://aclanthology.org/2022.bionlp-1.41.mp4