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Steps in Outbreak Investigation01:18

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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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Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data.

Yidi Qin1, Rebecca I Caldino Bohn1, Aditya Sriram1

  • 1Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA, United States.

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|February 16, 2023
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Pediatric sepsis requires personalized treatment. This review explores empiric and machine-learning phenotypes to improve precision medicine approaches, highlighting challenges in capturing sepsis heterogeneity for better outcomes.

Keywords:
biomarkerclusteringmachine-learningobservational datapediatric sepsis

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Area of Science:

  • Critical care medicine
  • Pediatric infectious diseases
  • Computational biology

Background:

  • Sepsis causes 20% of global deaths, with 3 million child deaths annually.
  • Current pediatric sepsis management often uses a "one-size-fits-all" approach, limiting treatment efficacy.
  • Precision medicine offers a promising avenue to tailor treatments for individual pediatric sepsis patients.

Purpose of the Study:

  • To review and compare empiric and machine-learning-based phenotyping strategies for pediatric sepsis.
  • To highlight the complexities of pediatric sepsis pathobiology and heterogeneity.
  • To identify methodological steps and challenges in developing precise pediatric sepsis phenotypes.

Main Methods:

  • Literature review of existing phenotyping strategies in pediatric sepsis.
  • Analysis of multifaceted data underlying sepsis pathobiology.
  • Comparison of empiric versus machine-learning-based phenotyping approaches.

Main Results:

  • Empiric and machine-learning phenotypes aid in accelerating diagnosis and treatment.
  • Neither current phenotyping strategy fully captures the heterogeneity of pediatric sepsis.
  • Significant challenges remain in accurately delineating pediatric sepsis phenotypes.

Conclusions:

  • Advancing precision medicine in pediatric sepsis necessitates improved phenotyping strategies.
  • Further research is needed to overcome challenges in capturing sepsis heterogeneity.
  • Accurate phenotyping is crucial for optimizing clinical outcomes in pediatric sepsis.