Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers
Shi Li
TL;DR
This work tackles the interpretability challenge of deep learning in healthcare by adopting Neural Ordinary Differential Equations (NODEs) for text classification. It argues that NODEs, with their continuous-time dynamics and adjoint-based training, provide transparent representations that can be interpreted via saliency maps and vector-field visualizations, demonstrated on hospital outcome prediction and Alzheimer's staging. The study shows NODEs achieve competitive accuracy while offering interpretable insights, highlighting potential benefits in memory efficiency and robustness to gradient issues. The findings suggest NODEs as a viable path toward transparent, data-efficient NLP models in healthcare, warranting further exploration and integration with advanced text systems.
Abstract
Deep Learning has emerged as one of the most significant innovations in machine learning. However, a notable limitation of this field lies in the ``black box" decision-making processes, which have led to skepticism within groups like healthcare and scientific communities regarding its applicability. In response, this study introduces a interpretable approach using Neural Ordinary Differential Equations (NODEs), a category of neural network models that exploit the dynamics of differential equations for representation learning. Leveraging their foundation in differential equations, we illustrate the capability of these models to continuously process textual data, marking the first such model of its kind, and thereby proposing a promising direction for future research in this domain. The primary objective of this research is to propose a novel architecture for groups like healthcare that require the predictive capabilities of deep learning while emphasizing the importance of model transparency demonstrated in NODEs.