Machine Learning Consensus Clustering Approach for Patients with Lactic Acidosis in Intensive Care Units.

ABSTRACT: Lactic acidosis is a heterogeneous condition with multiple underlying causes and associated outcomes. The use of multi-dimensional patient data to subtype lactic acidosis can personalize patient care. Machine learning consensus clustering may identify lactic acidosis subgroups with unique clinical profiles and outcomes. We used the Medical Information Mart for Intensive Care III database to abstract electronic medical record data from patients admitted to intensive care units (ICU) in a tertiary care hospital in the United States. We included patients who developed lactic acidosis (defined as serum lactate ≥ 4 mmol/L) within 48 h of ICU admission. We performed consensus clustering analysis based on patient characteristics, comorbidities, vital signs, organ supports, and laboratory data to

SUBMITTER: Pattharanitima P 

PROVIDER: S-EPMC8623582 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature