Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements.

ABSTRACT: The goal of this study was to categorize patients with abnormal serum phosphate upon hospital admission into distinct clusters utilizing an unsupervised machine learning approach, and to assess the mortality risk associated with these clusters. We utilized the consensus clustering approach on demographic information, comorbidities, principal diagnoses, and laboratory data of hypophosphatemia (serum phosphate ≤ 2.4 mg/dL) and hyperphosphatemia cohorts (serum phosphate ≥ 4.6 mg/dL). The standardized mean difference was applied to determine each cluster's key features. We assessed the association of the clusters with mortality. In the hypophosphatemia cohort (n = 3113), the consensus cluster analysis identified two clusters. The key features of patients in Cluster 2, compared with Cluster 1,

SUBMITTER: Thongprayoon C 

PROVIDER: S-EPMC8509302 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature