论文标题
幸存ODE：具有纵向数据竞争风险的基于神经ODE的事件模型可改善与癌症相关的静脉血栓栓塞（VTE）预测
SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Venous Thromboembolism (VTE) prediction
论文作者
论文摘要
从电子健康记录（EHR）数据中进行有效学习来预测临床结果，这通常是具有挑战性的，因为在不规则的时间段记录的特征和随访以及竞争性事件（例如死亡或疾病进展）中的特征。为此，我们提出了一种生成的事实模型，即Survlatent Ode，该模型采用了基于基于微分方程的复发性神经网络（ODE-RNN）作为编码器，以有效地将潜在状态的动力学在不规则采样的输入数据下进行参数化。然后，我们的模型利用所得的潜在嵌入来灵活地估算多个竞争事件的生存时间，而无需指定特定事件特定危险功能的形状。我们展示了我们的模型在Mimic-III上的竞争性能，这是一种从重症监护病房收集的纵向数据集，在预测医院的死亡率以及Dana-Farber癌症研究所（DFCI）的数据方面预测静脉动脉症（VTE）的复杂性（VTE），伴随着癌症患者的死亡患者，竞争癌症患者的毒死。 Survlatent Ode的表现优于当前分层VTE风险组的临床标准Khorana风险评分，同时提供临床上有意义且可解释的潜在表示。
Effective learning from electronic health records (EHR) data for prediction of clinical outcomes is often challenging because of features recorded at irregular timesteps and loss to follow-up as well as competing events such as death or disease progression. To that end, we propose a generative time-to-event model, SurvLatent ODE, which adopts an Ordinary Differential Equation-based Recurrent Neural Networks (ODE-RNN) as an encoder to effectively parameterize dynamics of latent states under irregularly sampled input data. Our model then utilizes the resulting latent embedding to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. We demonstrate competitive performance of our model on MIMIC-III, a freely-available longitudinal dataset collected from critical care units, on predicting hospital mortality as well as the data from the Dana-Farber Cancer Institute (DFCI) on predicting onset of Venous Thromboembolism (VTE), a life-threatening complication for patients with cancer, with death as a competing event. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying VTE risk groups, while providing clinically meaningful and interpretable latent representations.
