提出可解释的深度生存模型，预测糖尿病患者出院后足部并发症风险。
Interpretable Fine-Gray Deep Survival Model for Competing Risks: Predicting Post-Discharge Foot Complications for Diabetic Patients in Ontario
- 基于神经加性模型结构，为每种风险设计独立投影向量。
- 在安大略29家医院数据上实现与顶尖模型相当的预测性能。
- 通过形状函数和重要性图实现结果透明，适合临床医生验证。
模型可解释性对医疗AI安全及临床信任至关重要，尤其在竞争风险生存分析中。现有深度学习模型虽预测性能优异，但因黑箱特性难以融入临床实践。为此，我们提出一种内在可解释的生存模型CRISPNAM-FG，基于神经加性模型（NAMs）结构，为每种风险设计独立投影向量，采用Fine-Gray方法预测累积发生函数，在多个基准数据集上验证，并应用于安大略省29家医院2016–2023年糖尿病患者数据，以预测出院后足部并发症。该模型在保持高预测能力的同时，提供可审计的形状函数与特征重要性图，显著提升透明度。
原文摘要 · Abstract (English)
Model interpretability is crucial for establishing AI safety and clinician trust in medical applications for example, in survival modelling with competing risks. Recent deep learning models have attained very good predictive performance but their limited transparency, being black-box models, hinders their integration into clinical practice. To address this gap, we propose an intrinsically interpretable survival model called CRISPNAM-FG. Leveraging the structure of Neural Additive Models (NAMs) with separate projection vectors for each risk, our approach predicts the Cumulative Incidence Function using the Fine-Gray formulation, achieving high predictive power with intrinsically transparent and auditable predictions. We validated the model on several benchmark datasets and applied our model to predict future foot complications in diabetic patients across 29 Ontario hospitals (2016-2023). Our method achieves competitive performance compared to other deep survival models while providing transparency through shape functions and feature importance plots.
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