ArticleEntropy (Basel, Switzerland)2022
Discriminating Bacterial Infection from Other Causes of Fever Using Body Temperature Entropy Analysis.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 8 citations in OpenAlex.
- Continuous Temperature Monitoring via Wearable Devices for Fever or Infection Management in Acute Care Hospitals: Scoping Review of Clinical Implementation and Data Analytics.JMIR mHealth and uHealth · 2026Article
- Next generation bioelectronic medicine: making the case for non-invasive closed-loop autonomic neuromodulation.Bioelectronic medicine · 2025Review
- Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis.Entropy (Basel, Switzerland) · 2024Article
- Free-living core body temperature monitoring using a wrist-worn sensor after COVID-19 booster vaccination: a pilot study.Biomedical engineering online · 2023Article
- Slope Entropy Characterisation: The Role of theEntropy (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
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Abstract
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.
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Registered trials
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