jay koyner

Associate Professor of Medicine

University of Chicago

United States

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Dr. Jay Koyner is an Associate Professor of Medicine at the University of Chicago, USA. His research primarily focuses on acute kidney injury, utilizing artificial intelligence for treatment improvements, and the impact of sepsis on renal health. He has contributed significantly to the literature on kidney failure management, particularly in the context of the COVID-19 pandemic and dynamic risk assessment strategies.

Articles (13)

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2025

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2025

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2024

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2023

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2023

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2023

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2023

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2023

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2022

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2022

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2022

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2022

Collaborators (6)

Steven Coca

Associate Professor of Medicine

Icahn School of Medicine at Mount Sinai

UNITED STATES

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Matthew Churpek

Professor

University of Wisconsin-Madison

UNITED STATES

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John Prowle

Clinical Reader in Critical Care Nephrology

Queen Mary University of London

UNITED KINGDOM

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Samira Bell

Academic, Clinical Sen Lecturer/Honorary Cons (Teaching and Research)

University of Dundee

UNITED KINGDOM

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Yvelynne Kelly

Tallaght University Hospital

IRELAND

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Neesh Pannu

University of Alberta

CANADA

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jay koyner

Research Interests

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Articles (13)

Multicenter Development and Validation of a Multimodal Deep Learning Model to Predict Moderate to Severe AKI

Can we use artificial intelligence to better treat acute kidney injury?

10 tips on how to use dynamic risk assessment and alerts for AKI

Sepsis-associated acute kidney injury: consensus report of the 28th Acute Disease Quality Initiative workgroup.

Sepsis-associated acute kidney injury—treatment standard

Care of the Hospitalized Patients with Kidney Failure during COVID-19 Pandemic: Lessons Learned

Intraoperative Renal Replacement Therapy in Orthotopic Liver Transplantation

Cautious Optimism

Outcomes, Healthcare Resource Utilization, and Costs of Overall, Community-Acquired, and Hospital-Acquired Acute Kidney Injury in COVID-19 Patients

Healthcare resource utilization and costs of persistent severe AKI among hospitalized Stage 2/3 AKI patients

Artificial Intelligence in Acute Kidney Injury Prediction

Optimizing the Design and Analysis of Future AKI Trials

Performance of a Standardized Clinical Assay for Urinary C–C Motif Chemokine Ligand 14 (CCL14) for Persistent Severe Acute Kidney Injury

Collaborators (6)

Steven Coca

Matthew Churpek

John Prowle

Samira Bell

Yvelynne Kelly

Neesh Pannu

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