ArticleDiagnostics (Basel, Switzerland)2021
Machine Learning Consensus Clustering Approach for Hospitalized Patients with Dysmagnesemia.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 15 citations in OpenAlex.
- Hypomagnesemia With Metformin Use in Diabetes Mellitus: A Case and Narrative Review.Kidney medicine · 2025Review
- Trajectories of urea‑to‑creatinine ratio and risk of clinical outcomes in survivors of acute kidney disease: a population-based study.Clinical kidney journal · 2025Article
- Dysmagnesemia Incidence in Hospitalized Dogs and Cats: A Retrospective Study.Animals : an open access journal from MDPI · 2025Article
- Prevalence, clinical characteristics, and health outcomes of dysmagnesemia measured by ionized and total body concentrations among medically hospitalized patients.Scientific reports · 2024Article
- Association of early postoperative serum magnesium with acute kidney injury after cardiac surgery.Renal failure · 2023Article
- Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach.BMC cardiovascular disorders · 2023Article
- Incidence of Dysmagnesemia among Medically Hospitalized Patients and Associated Clinical Characteristics: A Prospective Cohort Study.International journal of endocrinology · 2023Article
- Clinical Phenotypes of Dual Kidney Transplant Recipients in the United States as Identified through Machine Learning Consensus Clustering.Medicina (Kaunas, Lithuania) · 2022Article
- Characteristics of Kidney Recipients of High Kidney Donor Profile Index Kidneys as Identified by Machine Learning Consensus Clustering.Journal of personalized medicine · 2022Article
Corrections and comments
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Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsConsensus 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.
resultsIn hypomagnesemia cohort (
conclusionOur cluster analysis identified clinically distinct phenotypes with differing mortality risks in hospitalized patients with dysmagnesemia. Future studies are required to assess the application of this ML consensus clustering approach to care for hospitalized patients with dysmagnesemia.
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