Leveraging explainable artificial intelligence for early prediction of bloodstream infections using historical electronic health records.

ABSTRACT: Bloodstream infections (BSIs) are a severe public health threat due to their rapid progression into critical conditions like sepsis. This study presents a novel eXplainable Artificial Intelligence (XAI) framework to predict BSIs using historical electronic health records (EHRs). Leveraging a dataset from St. Olavs Hospital in Trondheim, Norway, encompassing 35,591 patients, the framework integrates demographic, laboratory, and comprehensive medical history data to classify patients into high-risk and low-risk BSI groups. By avoiding reliance on real-time clinical data, our model allows for enhanced scalability across various healthcare settings, including resource-limited environments. The XAI framework significantly outperformed traditional models, particularly with tree-based algorithms,

SUBMITTER: Bopche R 

PROVIDER: S-EPMC11563427 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature