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Artificial Intelligence- and Machine Learning-Assisted Subphenotyping for Personalized Immunotherapy in Sepsis.

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Area of Science:

  • Critical Care Medicine
  • Immunology
  • Computational Biology

Background:

  • Sepsis heterogeneity hinders immunotherapy progress.
  • Artificial intelligence (AI) and machine learning (ML) offer novel approaches to analyze complex sepsis data.
  • Understanding sepsis subphenotypes is crucial for targeted treatment strategies.

Purpose of the Study:

  • To review existing evidence on AI-derived sepsis subtyping.
  • To explore treatment responses to immune modulating therapies based on sepsis subphenotypes.
  • To discuss the implications of AI in sepsis research.

Main Methods:

  • A structured literature search was performed on MEDLINE.
  • A narrative review of relevant publications was conducted.
  • The review focused on studies utilizing clinical, biological, and omics data for sepsis subtyping.

Main Results:

  • Multiple AI/ML algorithms for sepsis subtyping were identified.
  • The primary classification distinguished between hyper- and hypoinflammatory subphenotypes.
  • Statins, corticosteroids, activated protein C, and thrombomodulin showed differential effects across sepsis subphenotypes.

Conclusions:

  • Prospective validation of AI-derived sepsis subtypes is essential.
  • AI-driven subtyping holds potential for personalized immunotherapy in sepsis.
  • Addressing validity, equity, ethics, and feasibility is critical for clinical implementation.