Vargas Borja, Cuesta-Frau David, González-López Paula, Fernández-Cotarelo María-José, Vázquez-Gómez Óscar, Colás Ana, Varela Manuel
Department of Internal Medicine, Hospital Universitario de Móstoles, 28935 Mostoles, Spain.
Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain.
Entropy (Basel). 2022 Apr 5;24(4):510. doi: 10.3390/e24040510.
Body temperature is usually employed in clinical practice by strict binary thresholding, aiming to classify patients as having fever or not. In the last years, other approaches based on the continuous analysis of body temperature time series have emerged. These are not only based on absolute thresholds but also on patterns and temporal dynamics of these time series, thus providing promising tools for early diagnosis. The present study applies three time series entropy calculation methods (Slope Entropy, Approximate Entropy, and Sample Entropy) to body temperature records of patients with bacterial infections and other causes of fever in search of possible differences that could be exploited for automatic classification. In the comparative analysis, Slope Entropy proved to be a stable and robust method that could bring higher sensitivity to the realm of entropy tools applied in this context of clinical thermometry. This method was able to find statistically significant differences between the two classes analyzed in all experiments, with sensitivity and specificity above 70% in most cases.
在临床实践中，体温通常通过严格的二元阈值划分来使用，目的是将患者分类为发热或未发热。在过去几年中，出现了其他基于体温时间序列连续分析的方法。这些方法不仅基于绝对阈值，还基于这些时间序列的模式和时间动态，从而为早期诊断提供了有前景的工具。本研究将三种时间序列熵计算方法（斜率熵、近似熵和样本熵）应用于细菌感染患者和其他发热原因患者的体温记录，以寻找可能用于自动分类的差异。在比较分析中，斜率熵被证明是一种稳定且强大的方法，能够为临床体温测量领域应用的熵工具带来更高的灵敏度。该方法在所有实验中都能在分析的两类之间找到统计学上的显著差异，在大多数情况下灵敏度和特异性均高于70%。
