基于基因特征个性化建模疾病多阶段进展，提升临床预测准确性。
Genetics-Driven Personalized Disease Progression Model
- 用变分自编码器从基因数据中提取患者特征
- 结合RNN状态空间模型捕捉不同进展路径，优于传统统一轨迹方法
- 适合研究遗传异质性显著的慢性病，如癌症、糖尿病
慢性病（如癌症、糖尿病、慢性肾病）的多阶段疾病进展建模对临床决策至关重要。现有方法通常在人群层面假设统一的进展轨迹，但慢性病具有高度异质性，其进展模式受个体基因和生活方式等环境因素影响。本文提出一种个性化疾病进展模型，联合学习异质性进展模式与基因谱型分组。设计端到端流程：利用变分自编码器从基因标记中推断患者特征，并通过基于临床观测的RNN状态空间模型刻画其驱动的疾病进展。在真实世界与合成临床数据上均验证了模型有效性。
原文摘要 · Abstract (English)
Modeling disease progression through multiple stages is critical for clinical decision-making for chronic diseases, e.g., cancer, diabetes, chronic kidney diseases, and so on. Existing approaches often model the disease progression as a uniform trajectory pattern at the population level. However, chronic diseases are highly heterogeneous and often have multiple progression patterns depending on a patient's individual genetics and environmental effects due to lifestyles. We propose a personalized disease progression model to jointly learn the heterogeneous progression patterns and groups of genetic profiles. In particular, an end-to-end pipeline is designed to simultaneously infer the characteristics of patients from genetic markers using a variational autoencoder and how it drives the disease progressions using an RNN-based state-space model based on clinical observations. Our proposed model shows improvement on real-world and synthetic clinical data.
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