Hepatorenal Syndrome (HRS) is a critical complication of advanced liver disease characterized by the development of acute kidney injury in patients with cirrhosis. HRS is a complex condition with various clinical presentations and outcomes. The ability to categorize HRS into distinct subtypes can greatly enhance our understanding and management of the condition. In recent years, machine learning-based consensus clustering has emerged as a promising approach to identify HRS subtypes and uncover associated findings. This article delves into the various hepatorenal syndrome subtypes identified through machine learning techniques, exploring their clinical significance, treatment implications, and the potential for improved patient care.
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Hepatology and Pancreatic Science received 34 citations as per Google Scholar report
