信息理论揭示了与疾病症状密度相关的 COVID-19 生理表现

Jacob M. Ryan, Shreenithi Navaneethan, Natalie Damaso, Stephan Dilchert, W. Hartogensis, Joseph L. Natale, Frederick M. Hecht, Ashley Mason, Benjamin L. Smarr
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摘要

通过可穿戴传感设备检测 COVID-19 疾病的算法倾向于隐含地将该疾病视为造成与健康生理机能相背离的刻板印象(因此是可识别的)。与此相反,临床环境中对 SARS-CoV-2 感染的身体反应却呈现出极大的多样性。这就提出了一个问题:如何描述疾病表现的多样性,以及这种描述能否揭示不同疾病表现之间有意义的关系。在此,我们提出了一个以信息论为基础的框架,通过可穿戴设备(Oura Ring)提供的连续生理数据,生成疾病表现的量化图谱,我们称之为 "表现"。我们在先前报告的 COVID-19 阳性队列(N = 73)中,对报告发病时评估的五种生理数据流(心率、心率变异性、呼吸频率、代谢活动和睡眠温度)对这一框架进行了测试。我们发现,与所有可能表现的空间相比,该队列中不同表现的数量很少。此外,表现频率与特定个体在推测的发病前几天内报告的粗略症状数量相关。这些发现表明,信息理论方法可用于将 COVID-19 疾病表现分类为具有实际价值的类型。这一概念证明支持使用信息理论方法从连续的生理数据中绘制疾病表现图。如果在大量不同的样本中开发出这种方法,就有可能为算法设计和实时治疗决策提供依据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness
Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term “manifestations,” as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.
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