Clinical Trial: A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data
Study Status: COMPLETED
Recruit Status: COMPLETED
Condition: Liver Failure After Operative Procedure
Study Type: OBSERVATIONAL
Official Title: A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data
Brief Summary:
Post-hepatectomy liver failure (PHLF) is the leading cause of morbidity and mortality following major hepatectomy.Existing prediction models fail to capture the dynamic liver regeneration and perioperative changes, limiting their predictive accuracy.We aimed to develop a machine learning (ML) modelling system (PILOT architecture) integrating liver regeneration biomarkers with time-phased perioperative clinical data to accurately predict PHLF risk.
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