用微分方程模型提升医疗预测的可解释性
Exploring Neural Ordinary Differential Equations as Interpretable Healthcare classifiers
- 基于神经微分方程构建连续动态建模框架
- 首次实现对文本数据的连续处理，提升可解释性
- 适合需透明决策的医疗与科研场景
深度学习已成为机器学习领域最重要的创新之一。然而，其‘黑箱’决策过程引发医疗与科学界对其应用性的质疑。为此，本研究提出一种基于神经常微分方程（Neural Ordinary Differential Equations, NODEs）的可解释方法。该类神经网络模型利用微分方程的动力学特性进行表示学习。依托微分方程基础，我们展示了此类模型持续处理文本数据的能力，这是同类模型中的首次尝试，为该领域未来研究提供了有前景的方向。本研究的核心目标是为医疗等需要深度学习预测能力但强调模型透明性的领域，提出一种新型架构，彰显NODEs在可解释性方面的优势。
原文摘要 · Abstract (English)
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.
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