用因果干预训练提升糖尿病血糖预测模型可解释性
Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients
- 在神经网络中引入因果干预训练，强制模型学习专家知识中的因果关系
- 在不同餐后预测时长下，因果模型性能优于传统模型，误差更低
- 可通过反事实损失分解，定位模型对因果机制的捕捉不足之处
因果抽象技术如交换干预训练（IIT）已被提出用于将专家知识编码的因果模型注入神经网络，但其在真实场景中的应用仍有限。本文探索了IIT在1型糖尿病（T1DM）患者血糖水平预测中的应用。研究使用经FDA批准的无环simglucose仿真器训练多层感知机（MLP）模型，并采用IIT施加因果关系。结果显示，经过IIT训练的模型有效抽象了因果结构，在餐后不同预测时长（PH）下的预测性能均优于标准模型。此外，反事实损失的分解可用于解释模型在哪些因果机制上捕捉效果更好或较差。这些初步结果表明，IIT有望通过有效融入专家知识，提升医疗领域预测模型的性能与可解释性。
原文摘要 · Abstract (English)
Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge encoded in causal models, but their application to real-world problems remains limited. This article explores the application of IIT in predicting blood glucose levels in Type 1 Diabetes Mellitus (T1DM) patients. The study utilizes an acyclic version of the simglucose simulator approved by the FDA to train a Multi-Layer Perceptron (MLP) model, employing IIT to impose causal relationships. Results show that the model trained with IIT effectively abstracted the causal structure and outperformed the standard one in terms of predictive performance across different prediction horizons (PHs) post-meal. Furthermore, the breakdown of the counterfactual loss can be leveraged to explain which part of the causal mechanism are more or less effectively captured by the model. These preliminary results suggest the potential of IIT in enhancing predictive models in healthcare by effectively complying with expert knowledge.
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