论文标题
时间表型使用疾病进展的深度预测聚类
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
论文作者
论文摘要
由于现代电子健康记录的更广泛可用性，患者护理数据通常以时间序列的形式存储。聚类此类时间序列数据对于患者表型至关重要，通过鉴定“相似”患者以及设计针对均质患者亚组量身定制的治疗指南，可以预见患者的预后。在本文中，我们开发了一种深度学习方法，用于聚类时间序列数据，其中每个群集包括具有相似未来结果的患者（例如不良事件，合并症的发作）。为了鼓励每个集群具有均匀的未来结果，通过学习离散表示，可以最能根据新的损失功能来描述未来结果分布。两个现实世界数据集的实验表明，我们的模型可实现优于最先进基准测试的卓越群集性能，并确定可以将有意义的集群转化为可行的信息以进行临床决策。
Due to the wider availability of modern electronic health records, patient care data is often being stored in the form of time-series. Clustering such time-series data is crucial for patient phenotyping, anticipating patients' prognoses by identifying "similar" patients, and designing treatment guidelines that are tailored to homogeneous patient subgroups. In this paper, we develop a deep learning approach for clustering time-series data, where each cluster comprises patients who share similar future outcomes of interest (e.g., adverse events, the onset of comorbidities). To encourage each cluster to have homogeneous future outcomes, the clustering is carried out by learning discrete representations that best describe the future outcome distribution based on novel loss functions. Experiments on two real-world datasets show that our model achieves superior clustering performance over state-of-the-art benchmarks and identifies meaningful clusters that can be translated into actionable information for clinical decision-making.
