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Jun Kim

ResearcherPublications, citations & collaboration network

Jun Kim is a researcher indexed in ScholarIQ from OpenAlex & ORCID. ScholarIQ records 380 works, 8,257 citations, an h-index of 51 and an i10-index of 157.

380
Works
8,257
Citations
51
h-index
157
i10-index

How has Jun Kim's publication output changed over time?

ScholarIQpublication output · 2004–2024

Output grew0% over the shown period — from 1 works in 2004 to 1 in 2024.

1
1
1
1
2
3
1
1
20042010201420152017201820192024

What are the most-cited papers on Jun Kim?

ScholarIQmost cited works
Automated deep-neural-network surveillance of cranial images for acute neurologic events
J. Titano, Marcus A. Badgeley, Javin Schefflein, Margaret Pain, Andres Su, Michael Cai, Nathaniel Swinburne, John R. Zech, Jun Kim, Joshua B. Bederson, J Mocco, Burton P. Drayer, Joseph Lehár, Samuel K. Cho, Anthony Costa, Eric K. Oermann
Nature Medicine. 2018466 Citations
Rapamycin Markedly Slows Disease Progression in a Rat Model of Polycystic Kidney Disease
Yunxia Tao, Jun Kim, Robert W. Schrier, Charles L. Edelstein
Journal of the American Society of Nephrology. 2004387 Citations
Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion
Jun Kim, Robert Merrill, Varun Arvind, Deepak Kaji, Sara Pasik, Chuma C. Nwachukwu, Luilly Vargas, Nebiyu Osman, Eric K. Oermann, John M. Caridi, Samuel K. Cho
Spine. 2017212 Citations
An attention based deep learning model of clinical events in the intensive care unit
Deepak Kaji, John R. Zech, Jun Kim, Samuel K. Cho, Neha Dangayach, Anthony Costa, Eric K. Oermann
PLoS ONE. 2019203 CitationsOPEN ACCESS
CO2 capture by amine-functionalized nanoporous materials: A review
Chao Chen, Jun Kim, Wha‐Seung Ahn
S197055754. 2014184 Citations

Related on ScholarIQ

Chungnam National University
Institution
Automated deep-neural-network surveillance of cranial images for acute neurologic events
Paper
Rapamycin Markedly Slows Disease Progression in a Rat Model of Polycystic Kidney Disease
Paper
Examining the Ability of Artificial Neural Networks Machine Learning Models to Accurately Predict Complications Following Posterior Lumbar Spine Fusion
Paper
An attention based deep learning model of clinical events in the intensive care unit
Paper
CO2 capture by amine-functionalized nanoporous materials: A review
Paper
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