摘要翻译：
基于信号的疾病早期检测一直是研究和医院环境中的一个关键主题；它降低了技术成本，为快速有效的病人护理操作铺平了道路。基本的机器学习和信号处理算法已经被证明足以在临床症状出现之前对病毒和细菌疾病的发作进行分类。受这些最新发展的启发，这个项目使用信号动力学分析来推断生命体征（温度、呼吸和心率）的变化。结果表明，一个重要功能的变化趋势可以从另一个重要功能的变化趋势中预测出来。特别是，它显示心率和呼吸通常在体温后不久改变，主动脉血压随之改变。这并不是一种针对病因的方法，但如果进一步推进，它可以使患者和可穿戴系统用户驯服这些变化，并防止立即出现症状。
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英文标题：
《Exploring the Synchrony Between Body Temperature and HR, RR, and Aortic
  Blood Pressure in Viral/Bacterial Disease Onsets with Signal Dynamics》
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作者：
Camille Dunning
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最新提交年份：
2020
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分类信息：

一级分类：Quantitative Biology        数量生物学
二级分类：Other Quantitative Biology        其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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英文摘要：
  Signal-based early detection of illnesses has been a key topic in research and hospital settings; it reduces technological costs and paves the way for quick and effective patient-care operations. Elementary machine learning and signal processing algorithms have proven to be sufficient in classifying the onset of viral and bacterial conditions before clinical symptoms are shown. Inspired by these recent developments, this project employs signal dynamics analysis to infer changes in vital signs (temperature, respiration, and heart rate). The results demonstrate that the trends of one vital function can be predicted from that of another. In particular, it is shown that heart rate and respiration typically change shortly after body temperature, and aortic blood pressure follows. This is not an etiologically specific approach, but if advanced further, it can enable patients and wearable system users to tame these changes and prevent immediate symptoms.
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PDF链接：
https://arxiv.org/pdf/2011.01877