Biomarkers of Infection and Sepsis.

Area of Science:

• Biomarker discovery and computational diagnostics in infectious diseases.

Background:

• Sepsis diagnosis lacks specificity due to its nature as a syndrome.
• Current diagnostic methods lack a universally agreed-upon definition or gold standard.
• Expert opinion serves as the current, albeit imperfect, diagnostic benchmark.

Purpose of the Study:

• To review the evolution of sepsis detection biomarkers.
• To highlight the potential of machine learning in improving sepsis diagnosis.

Main Methods:

• Review of current literature on molecular biomarkers for sepsis.
• Exploration of machine learning and big data analytics in sepsis research.

Main Results:

• Molecular biomarkers are increasingly important in sepsis detection.
• Machine learning and big data offer promising avenues for more accurate sepsis diagnosis.

Conclusions:

• The diagnostic precision of sepsis remains a significant challenge.
• Machine learning holds the potential to refine or even replace expert opinion in defining sepsis.