Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.

ABSTRACT: The objectives of this study were to classify patients with serum magnesium derangement on hospital admission into clusters using unsupervised machine learning approach and to evaluate the mortality risks among these distinct clusters. Consensus cluster analysis was performed based on demographic information, principal diagnoses, comorbidities, and laboratory data in hypomagnesemia (serum magnesium ≤ 1.6 mg/dL) and hypermagnesemia cohorts (serum magnesium ≥ 2.4 mg/dL). Each cluster's key features were determined using the standardized mean difference. The associations of the clusters with hospital mortality and one-year mortality were assessed. In hypomagnesemia cohort (n = 13,320), consensus cluster analysis identified three clusters. Cluster 1 patients had the highest comorbidity burden

SUBMITTER: Thongprayoon C 

PROVIDER: S-EPMC8619519 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature