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Machine Learning for Predicting Pediatric Mortality After Cardiopulmonary Bypass: Harnessing the Complex Predictive Power of Accessible Inflammatory Markers

2026-06-29

Abstract excerpt

<title>Abstract</title> <p> <bold>Purpose</bold> Patients undergoing cardiopulmonary bypass frequently develop systemic inflammatory response syndrome, ranging from postoperative inflammation to multiple organ dysfunction syndrome and death. The aim of this study was to evaluate the predictive capacity of complete blood count parameters and other low-cost, readily available inflammatory markers for 30-day morta...

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Literature Corpus work
9c25fd61-cd73-5fb7-b908-a5cab49ca5e6
DOI
10.21203/rs.3.rs-10009456/v1
Open publication

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Machine Learning for Predicting Pediatric Mortality After Cardiopulmonary Bypass: Harnessing the Complex Predictive Power of Accessible Inflammatory MarkersDOI 10.21203/rs.3.rs-10009456/v1
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