论文标题
患有慢性复杂疾病的患者轨迹的表型簇
Phenotyping Clusters of Patient Trajectories suffering from Chronic Complex Disease
论文作者
论文摘要
近年来，由于电子患者数据的可用性，由于预测医院的住院住院风险和轨迹演变的任务增加了重点。解决这些问题的一种常见方法涉及将患者的时间序列信息（例如生命体征观察）确定以确定患者人群的相似亚组。大多数聚类方法都采用生命符号的时间不变，并且无法在临床上相关的集群中提供可解释性，例如事件或结果信息。在这项工作中，我们评估了患有慢性阻塞性肺部疾病的患者的大型医院数据集中的三种不同的聚类模型。我们进一步提出了新的修改，以处理不均匀的抽样时间序列数据和不平衡的类别分布以改善表型分离。最后，我们讨论了模型的进一步研究途径，以学习具有不同行为和表型的患者亚组。
Recent years have seen an increased focus into the tasks of predicting hospital inpatient risk of deterioration and trajectory evolution due to the availability of electronic patient data. A common approach to these problems involves clustering patients time-series information such as vital sign observations) to determine dissimilar subgroups of the patient population. Most clustering methods assume time-invariance of vital-signs and are unable to provide interpretability in clusters that is clinically relevant, for instance, event or outcome information. In this work, we evaluate three different clustering models on a large hospital dataset of vital-sign observations from patients suffering from Chronic Obstructive Pulmonary Disease. We further propose novel modifications to deal with unevenly sampled time-series data and unbalanced class distribution to improve phenotype separation. Lastly, we discuss further avenues of investigation for models to learn patient subgroups with distinct behaviour and phenotype.
