Stephen B Erickson
Overview
Explore the profile of Stephen B Erickson including associated specialties, affiliations and a list of published articles.
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68
Citations
1400
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Recent Articles
1.
Sheikh M, Barreto E, Miao J, Thongprayoon C, Gregoire J, Dreesman B, et al.
Digit Health
. 2024 Apr;
10:20552076241248082.
PMID: 38638404
Background: This study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite...
2.
3.
Singh P, Bentall A, Erickson S
Kidney360
. 2022 Apr;
1(6):582-583.
PMID: 35368604
No abstract available.
4.
Thongprayoon C, Mao M, Kattah A, Keddis M, Pattharanitima P, Erickson S, et al.
Clin Kidney J
. 2022 Feb;
15(2):253-261.
PMID: 35145640
Background: Hospitalized patients with hypokalemia are heterogeneous and cluster analysis, an unsupervised machine learning methodology, may discover more precise and specific homogeneous groups within this population of interest. Our study...
5.
Thongprayoon C, Sy-Go J, Nissaisorakarn V, Dumancas C, Keddis M, Kattah A, et al.
Diagnostics (Basel)
. 2021 Nov;
11(11).
PMID: 34829467
Background: 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...
6.
Cogal A, Arroyo J, Shah R, Reese K, Walton B, Reynolds L, et al.
Kidney Int Rep
. 2021 Nov;
6(11):2862-2884.
PMID: 34805638
Introduction: Because of phenotypic overlap between monogenic urinary stone diseases (USD), gene-specific analyses can result in missed diagnoses. We used targeted next generation sequencing (tNGS), including known and candidate monogenic...
7.
Pinto E Vairo F, Prochnow C, Kemppainen J, Lisi E, Steyermark J, Kruisselbrink T, et al.
Kidney Med
. 2021 Nov;
3(5):785-798.
PMID: 34746741
Rationale & Objective: The etiology of kidney disease remains unknown in many individuals with chronic kidney disease (CKD). We created the Mayo Clinic Nephrology Genomics Clinic to improve our ability...
8.
Machine Learning Consensus Clustering Approach for Hospitalized Patients with Phosphate Derangements
Thongprayoon C, Dumancas C, Nissaisorakarn V, Keddis M, Kattah A, Pattharanitima P, et al.
J Clin Med
. 2021 Oct;
10(19).
PMID: 34640457
Background: 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...
9.
Thongprayoon C, Mao M, Keddis M, Kattah A, Chong G, Pattharanitima P, et al.
J Nephrol
. 2021 Oct;
35(3):921-929.
PMID: 34623631
Background: The objective of this study was to characterize hypernatremia patients at hospital admission into clusters using an unsupervised machine learning approach and to evaluate the mortality risk among these...
10.
Thongprayoon C, Nissaisorakarn V, Pattharanitima P, Mao M, Kattah A, Keddis M, et al.
Medicina (Kaunas)
. 2021 Sep;
57(9).
PMID: 34577826
: Despite the association between hyperchloremia and adverse outcomes, mortality risks among patients with hyperchloremia have not consistently been observed among all studies with different patient populations with hyperchloremia. The...