Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
Contextual Motifs: Increasing the Utility of Motifs using Contextual Data
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上下文主题：使用上下文数据增加主题的实用性
DOI：
10.1145/3097983.3098068
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
J. Wiens
中科院分区：
文献类型：
--
作者：
Ian Fox;Lynn Ang;M. Jaiswal;R. Pop;J. Wiens
Motif是分析生理波形数据的有力工具。然而，标准的Motif方法忽略了重要的上下文信息(例如，收集数据时患者正在做什么)。我们假设，这些额外的上下文数据可能会增加主题的效用。因此，我们提出了一种结合上下文的主题--语境主题的扩展。认识到语境往往是不可观察或不可用的，我们把重点放在联合推断主题和语境的方法上。应用于模拟和真实的生理数据，我们提出的方法在发现的模体的区分效用方面改进了现有的模体方法。特别是，我们在从1型糖尿病患者收集的连续血糖监测仪(CGM)数据中发现了上下文基序。与无上下文的对应对象相比，这些上下文主题导致了对低血糖和高血糖事件的更好预测。我们的结果表明，即使在推断时，在处理和解释生理波形数据时，上下文在长期和短期预测范围内都是有用的。
Motifs are a powerful tool for analyzing physiological waveform data. Standard motif methods, however, ignore important contextual information (e.g., what the patient was doing at the time the data were collected). We hypothesize that these additional contextual data could increase the utility of motifs. Thus, we propose an extension to motifs, contextual motifs, that incorporates context. Recognizing that, oftentimes, context may be unobserved or unavailable, we focus on methods to jointly infer motifs and context. Applied to both simulated and real physiological data, our proposed approach improves upon existing motif methods in terms of the discriminative utility of the discovered motifs. In particular, we discovered contextual motifs in continuous glucose monitor (CGM) data collected from patients with type 1 diabetes. Compared to their contextless counterparts, these contextual motifs led to better predictions of hypo- and hyperglycemic events. Our results suggest that even when inferred, context is useful in both a long- and short-term prediction horizon when processing and interpreting physiological waveform data.