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

Rajeev Bopche, Lise Tuset Gustad, Jan Egil Afset, Birgitta Ehrnström, Jan Kristian Damås and Øystein Nytrø

PLOS Digital Health, 2024, vol. 3, issue 11, 1-32

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, demonstrating superior specificity and sensitivity in BSI prediction. This approach promises to optimize resource allocation and potentially reduce healthcare costs while providing interpretability for clinical decision-making, making it a valuable tool in hospital systems for early intervention and improved patient outcomes.Author summary: In this research, we have developed a new tool that uses artificial intelligence to better predict bloodstream infections, which can lead to severe conditions like sepsis if not quickly identified and treated. It is the first-of-its-kind framework that analyzes past health records and helps identify patients at high risk of infection more accurately than existing tools. Unlike existing tools, our framework can be implemented at any stage of the patient trajectory and is the only framework to achieve good accuracy without the use of intimate patient features such as vital signs and real-time data, which may limit clinical applicability. This ability could enable doctors to prioritize care more pre-emptively and effectively, potentially saving lives and reducing unnecessary medical tests. Our approach is designed to be easily understood and used by medical professionals and those with little technical expertise, making it a valuable addition to hospital systems.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pdig00:0000506

DOI: 10.1371/journal.pdig.0000506

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