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Related Concept Videos

Patterns of Fever01:26

Patterns of Fever

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Before understanding the types and patterns of fever, it is essential to know its phases.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Assessing Body Temperature - Temporal Artery01:19

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Here is a stepwise guide to assessing the body temperature at the temporal artery using a temporal artery thermometer
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Run charts, essentially line graphs plotted over time, serve as fundamental yet effective tools for process analysis. They chronicle data sequentially, facilitating the identification of trends, shifts, or cyclical movements. This graphical representation is instrumental in determining whether a process is stable or exhibits signs of potential instability indicative of special cause variation. In the healthcare domain, run charts depict infection rates over time, enabling hospitals to monitor...
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A thermometer measures body temperature. The common sites for measuring body temperature are the oral cavity, axillary region, temporal artery, and skin surface, such as the forehead, abdomen, and axilla. True core body temperature is assessed in the rectum, tympanic membrane, pulmonary artery, esophagus, and urinary bladder.
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Fever can be triggered by several factors, including infections, nervous system disorders, certain cancers, blood diseases like leukemia, embolism, thrombosis, heatstroke, dehydration, surgical trauma, crushing injuries, and allergic reactions.
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Updated: Nov 27, 2025

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Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis.

David Cuesta-Frau1, Pradeepa H Dakappa2, Chakrapani Mahabala3

  • 1Technological Institute of Informatics, Universitat Politècnica de València, Alcoi Campus, 03801 Alcoi, Spain.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary

Analyzing continuous fever patterns using Slope Entropy can accurately diagnose diseases like malaria and dengue. This novel approach offers a promising tool for early disease detection based on body temperature fluctuations.

Keywords:
Matthews Correlation CoefficientSlope Entropybody temperaturedenguedifferential diagnosisfevermalariatime series classification

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Area of Science:

  • Medical Informatics
  • Biophysics
  • Computational Biology

Background:

  • Traditional fever diagnosis relies on simple temperature thresholds, often missing crucial information from temporal variations.
  • Continuous body temperature data offers a richer source of information for understanding disease status.

Purpose of the Study:

  • To develop and validate a method for diagnosing specific fever causes using continuous body temperature time series analysis.
  • To explore the utility of Slope Entropy for differentiating between various fever-inducing diseases.

Main Methods:

  • Application of Slope Entropy, a measure of pattern relative frequency, to continuous body temperature time series data.
  • Classification analysis of patient records, specifically distinguishing between dengue and malaria.
  • Input parameter customization and validation using the Matthews Correlation Coefficient.

Main Results:

  • The proposed method achieved high accuracy in classifying fever causes, with over 90% correct labeling in some cases.
  • Demonstrated the feasibility of using temporal body temperature patterns for disease diagnosis.
  • Slope Entropy proved effective in differentiating between diseases like dengue and malaria.

Conclusions:

  • Continuous body temperature time series analysis, particularly using Slope Entropy, is a feasible and accurate method for diagnosing fever causes.
  • This approach holds significant promise as an early diagnostic tool, especially relevant in scenarios like the COVID-19 pandemic.
  • Further research combining Slope Entropy with other measures like Sample Entropy could enhance its diagnostic capabilities.