使用疾病进展的深度预测聚类进行时间表型分析
Temporal Phenotyping using Deep Predictive Clustering of Disease Progression
摘要：
由于现代电子健康记录的广泛可用性，患者护理数据通常以时间序列的形式存储。对这些时间序列数据进行聚类对于患者表型分析、通过识别“相似”患者来预测患者的病情以及设计针对同质患者亚组的治疗指南至关重要。在本文中，我们开发了一种用于聚类时间序列数据的深度学习方法，其中每个聚类包括具有相似未来感兴趣结果的患者（例如，不良事件、合并症的发作）。为了鼓励每个聚类具有同质的未来结果，聚类通过学习离散表示来进行，离散表示基于新的损失函数最好地描述未来结果分布。在两个真实数据集上的实验表明，我们的模型在最先进的基准测试中实现了卓越的聚类性能，并识别出了有意义的聚类，这些聚类可以转化为临床决策的可操作信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
