Novel approach exploring the correlation between presepsin and routine laboratory parameters using explainable artificial intelligence.

ABSTRACT: Although presepsin, a crucial biomarker for the diagnosis and management of sepsis, has gained prominence in contemporary medical research, its relationship with routine laboratory parameters, including demographic data and hospital blood test data, remains underexplored. This study integrates machine learning with explainable artificial intelligence (XAI) to provide insights into the relationship between presepsin and these parameters. Advanced machine learning classifiers provide a multilateral view of data and play an important role in highlighting the interrelationships between presepsin and other parameters. XAI enhances analysis by ensuring transparency in the model's decisions, especially in selecting key parameters that significantly enhance classification accuracy. Utilizing XAI,

SUBMITTER: Jeong JS 

PROVIDER: S-EPMC11255511 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature