论文标题
Stagenet：阶段意识到健康风险预测的神经网络
StageNet: Stage-Aware Neural Networks for Health Risk Prediction
论文作者
论文摘要
深度学习在健康风险预测方面取得了成功，尤其是对于患有慢性和进步状况的患者。大多数现有的作品都集中在学习疾病网络（Stagenet）模型上，以从患者数据中提取疾病阶段信息并将其整合到风险预测中。 （1）阶段意识的长期记忆（LSTM）模块可以启用Stagenet，该模块不受监管提取健康阶段变化； （2）一个阶段自适应卷积模块，该模块将与阶段相关的进程模式纳入风险预测中。我们在两个现实世界数据集上评估Stagenet，并表明Stagenet在风险预测任务和患者亚型任务中的表现优于最先进的模型。与最佳的基线模型相比，Stagenet在两个现实世界中的患者数据集上实现了风险预测任务的AUPRC高达12％。 Stagenet还达到了Calinski-Harabasz的评分（群集质量度量），用于患者亚型的任务。
Deep learning has demonstrated success in health risk prediction especially for patients with chronic and progressing conditions. Most existing works focus on learning disease Network (StageNet) model to extract disease stage information from patient data and integrate it into risk prediction. StageNet is enabled by (1) a stage-aware long short-term memory (LSTM) module that extracts health stage variations unsupervisedly; (2) a stage-adaptive convolutional module that incorporates stage-related progression patterns into risk prediction. We evaluate StageNet on two real-world datasets and show that StageNet outperforms state-of-the-art models in risk prediction task and patient subtyping task. Compared to the best baseline model, StageNet achieves up to 12% higher AUPRC for risk prediction task on two real-world patient datasets. StageNet also achieves over 58% higher Calinski-Harabasz score (a cluster quality metric) for a patient subtyping task.
