Sepsis biomarkers and diagnostic tools with a focus on machine learning

Area of Science:

• Biomedical Informatics
• Computational Biology
• Clinical Data Science

Background:

• Sepsis diagnosis and management are complex, necessitating advanced analytical approaches.
• Data-driven techniques, including machine learning, show promise in improving sepsis care.
• Biomarkers and digital signatures are key targets for enhancing sepsis recognition and characterization.

Purpose of the Study:

• To review machine learning techniques applied to sepsis.
• To highlight applications in sepsis diagnostic tool development.
• To discuss the role of machine learning in identifying sepsis biomarkers.

Main Methods:

• Narrative review of published literature.
• Focus on supervised and unsupervised machine learning methods.
• Analysis of machine learning applications in sepsis biomarker discovery and diagnostic tool creation.

Main Results:

• Machine learning facilitates the discovery and evaluation of sepsis biomarkers and digital signatures.
• These data-driven approaches can improve diagnostic accuracy and timeliness.
• Machine learning models integrating biomarkers and clinical data are crucial for complex sepsis cases.

Conclusions:

• Machine learning is pivotal for advancing sepsis diagnostics and personalized medicine.
• Continued research in machine learning applications is essential for optimizing sepsis management.
• The identification of robust biomarkers through machine learning holds significant therapeutic potential.