Identifying acute kidney injury subtypes based on serum electrolyte data in ICU via K-medoids clustering

Presentation Time: 04:15 PM - 04:30 PM

Abstract Keywords: Precision Medicine, Data Mining, Machine Learning
Primary Track: Applications
Programmatic Theme: Clinical Informatics

This study proposes to use the K-medoids clustering method to identify subtypes of Intensive Care Unit (ICU)-acquired acute kidney injury (AKI) patients based on serum electrolyte data. Three distinct AKI subtypes with different serum electrolyte characteristics were identified by clustering analysis. Further, descriptive analysis was employed to characterize in-hospital mortality and renal replacement therapy, diuretic and vasopressor usage in the three subtypes, and Chi-square tests were conducted to check the differences of prognosis and treatments among the identified subtypes. This study enables the subclassification of AKI patients in the ICU, facilitating ICU physicians to make timely clinical decisions about AKI, and ultimately may contribute to patient outcome improvement.

Speaker(s):
Guilan Kong, PhD
National Institute of Health Data Science, Peking University