Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US

Authors

Vernon M. Chinchilli (ORCID: https://orcid.org/0000-0001-6488-7809)
Nasrollah Ghahramani (ORCID: https://orcid.org/0000-0002-0299-9394)
Djibril M. Ba (ORCID: https://orcid.org/0000-0002-5598-636X)
Yue Zhang (ORCID: https://orcid.org/0000-0001-6875-0025)
Rui Li (ORCID: https://orcid.org/0000-0002-7669-4112)

Institutions

Pennsylvania State University (US)
University of Pittsburgh (US)
Department of Public Health (MM)
Penn State Milton S. Hershey Medical Center (US)

Publication Details

Journal: EBioMedicine
Published: 2025-04-26
DOI: https://doi.org/10.1016/j.ebiom.2025.105726
Citations: 6
Primary Topic: Sepsis Diagnosis and Treatment
Type: article
Field-Weighted Citation Impact: 8.13

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article

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US

Vernon M. Chinchilli, Nasrollah Ghahramani, Djibril M. Ba, Yue Zhang et al.

Apr 26, 2025

6 citations

EBioMedicine

Sepsis Diagnosis and Treatment

8.13

article

Prediction of acute and chronic kidney diseases during the post-covid-19 pandemic with machine learning models: utilizing national electronic health records in the US

Vernon M. Chinchilli, Nasrollah Ghahramani, Djibril M. Ba, Yue Zhang, Rui Li

April 26, 2025article en

6 citations

Abstract

No abstract available for this paper.

EBioMedicineVol. 115

Pennsylvania State University (US), University of Pittsburgh (US), Department of Public Health (MM), Penn State Milton S. Hershey Medical Center (US)

Good health and well-being

Openalex Percentile: Top 2%

Sepsis Diagnosis and Treatment

8.13

DOI Open Access PDF OpenAlex

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