Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study.
Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study.
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DOI：
10.1038/s41598-018-22578-1
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发表时间：
2018-03-09
影响因子：
4.6
通讯作者：
Furlong LI
中科院分区：
文献类型：
--
作者：
Giannoula A;Gutierrez-Sacristán A;Bravo Á;Sanz F;Furlong LI
在基于人群的研究中，时间是评估合并症的关键参数，因为它允许识别除成对疾病关联之外的更复杂的疾病模式。到目前为止，它要么被完全忽略，要么只是通过评估已识别的合并症对的时间方向性来考虑。在这项工作中，为大规模合并症研究提出了一种新颖的时间分析框架。西班牙区域健康数据集患者的疾病史向量表示为有序疾病诊断的时间序列。确定具有统计显着性的成对疾病关联并评估其时间方向性。随后，将基于动态时间扭曲的无监督聚类算法应用于常见疾病轨迹，以便根据它们共享的时间模式对它们进行分组。所提出的对此类轨迹进行时间评估的方法可以作为疾病预测系统的初步基础。
Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanish health dataset are represented as time sequences of ordered disease diagnoses. Statistically significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, an unsupervised clustering algorithm, based on Dynamic Time Warping, is applied on the common disease trajectories in order to group them according to the temporal patterns that they share. The proposed methodology for the temporal assessment of such trajectories could serve as the preliminary basis of a disease prediction system.
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通讯作者：
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