Ki Moo Lim

1.1k total citations
84 papers, 702 citations indexed

About

Ki Moo Lim is a scholar working on Cardiology and Cardiovascular Medicine, Biomedical Engineering and Molecular Biology. According to data from OpenAlex, Ki Moo Lim has authored 84 papers receiving a total of 702 indexed citations (citations by other indexed papers that have themselves been cited), including 65 papers in Cardiology and Cardiovascular Medicine, 26 papers in Biomedical Engineering and 18 papers in Molecular Biology. Recurrent topics in Ki Moo Lim's work include Cardiac electrophysiology and arrhythmias (44 papers), ECG Monitoring and Analysis (26 papers) and Mechanical Circulatory Support Devices (16 papers). Ki Moo Lim is often cited by papers focused on Cardiac electrophysiology and arrhythmias (44 papers), ECG Monitoring and Analysis (26 papers) and Mechanical Circulatory Support Devices (16 papers). Ki Moo Lim collaborates with scholars based in South Korea, Indonesia and United States. Ki Moo Lim's co-authors include Da Un Jeong, Yunendah Nur Fuadah, Eun Bo Shim, Han‐Jeong Hwang, Natalia A. Trayanova, Eun Bo Shim, Kwang Soup Song, Renjun Zhu, Jason Constantino and Edward Andò and has published in prestigious journals such as Journal of Clinical Oncology, Scientific Reports and IEEE Transactions on Biomedical Engineering.

In The Last Decade

Ki Moo Lim

76 papers receiving 687 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ki Moo Lim South Korea 15 448 256 117 106 64 84 702
Matthias A. F. Gsell Austria 15 590 1.3× 204 0.8× 53 0.5× 70 0.7× 17 0.3× 37 824
Elias Karabelas Austria 12 428 1.0× 132 0.5× 45 0.4× 46 0.4× 12 0.2× 16 596
C. Zywietz Germany 10 484 1.1× 184 0.7× 65 0.6× 30 0.3× 125 2.0× 48 613
Eric Helfenbein United States 14 673 1.5× 159 0.6× 148 1.3× 43 0.4× 101 1.6× 50 832
C. Rajagopalan India 8 462 1.0× 153 0.6× 45 0.4× 61 0.6× 216 3.4× 28 597
Thomas Breen United States 14 95 0.2× 85 0.3× 68 0.6× 71 0.7× 50 0.8× 40 654
Kazuo Nakazawa Japan 18 462 1.0× 92 0.4× 56 0.5× 152 1.4× 34 0.5× 67 806
Marina Strocchi United Kingdom 13 501 1.1× 132 0.5× 47 0.4× 33 0.3× 6 0.1× 56 627
Eun Bo Shim South Korea 19 842 1.9× 251 1.0× 501 4.3× 75 0.7× 33 0.5× 78 1.3k
Xitian Pi China 13 146 0.3× 231 0.9× 23 0.2× 62 0.6× 69 1.1× 40 614

Countries citing papers authored by Ki Moo Lim

Since Specialization
Citations

This map shows the geographic impact of Ki Moo Lim's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ki Moo Lim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ki Moo Lim more than expected).

Fields of papers citing papers by Ki Moo Lim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ki Moo Lim. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ki Moo Lim. The network helps show where Ki Moo Lim may publish in the future.

Co-authorship network of co-authors of Ki Moo Lim

This figure shows the co-authorship network connecting the top 25 collaborators of Ki Moo Lim. A scholar is included among the top collaborators of Ki Moo Lim based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ki Moo Lim. Ki Moo Lim is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Fuadah, Yunendah Nur, et al.. (2025). Validation of new AI-based classification method for in silico cardiac safety assessment of drugs following the CiPA framework. Archives of Toxicology. 99(9). 3735–3749.
2.
Lim, Ki Moo, et al.. (2025). Association Between Geriatric Oral Health Assessment Index and Cardiovascular Disease in Korean Older Adults. Journal of Preventive Medicine and Public Health. 58(1). 103–112.
3.
Fuadah, Yunendah Nur, et al.. (2024). Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation. Scientific Reports. 14(1). 24045–24045. 5 indexed citations
4.
Jeong, Da Un, et al.. (2023). Application of Convolutional Neural Networks Using Action Potential Shape for In-Silico Proarrhythmic Risk Assessment. Biomedicines. 11(2). 406–406. 5 indexed citations
5.
Fuadah, Yunendah Nur, et al.. (2023). Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variability. Frontiers in Physiology. 14. 1266084–1266084. 6 indexed citations
6.
Yoon, Seung‐Hyun, et al.. (2023). Assessment of the proarrhythmic effects of repurposed antimalarials for COVID-19 treatment using a comprehensive in vitro proarrhythmia assay (CiPA). Frontiers in Pharmacology. 14. 1220796–1220796. 1 indexed citations
7.
Lim, Ki Moo, et al.. (2021). Sensitivity Analysis of Ion Channel Conductance on Myocardial Electromechanical Delay: Computational Study. Frontiers in Physiology. 12. 697693–697693. 3 indexed citations
8.
Jeong, Da Un, et al.. (2020). Computational Study to Identify the Effects of the KCNJ2 E299V Mutation in Cardiac Pumping Capacity. Computational and Mathematical Methods in Medicine. 2020. 1–11. 4 indexed citations
9.
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11.
Lim, Ki Moo, et al.. (2017). The effect of electrical conductivity of myocardium on cardiac pumping efficacy: a computational study. BioMedical Engineering OnLine. 16(1). 11–11. 5 indexed citations
12.
Shim, Eun Bo, et al.. (2016). Computational simulations of the effects of the G229D KCNQ1 mutation on human atrial fibrillation. The Journal of Physiological Sciences. 66(5). 407–415. 9 indexed citations
13.
Kim, Yooseok, et al.. (2016). Mathematical analysis of the effects of valvular regurgitation on the pumping efficacy of continuous and pulsatile left ventricular assist devices. Integrative Medicine Research. 5(1). 22–29. 6 indexed citations
14.
Kim, Nari, et al.. (2014). Computational prediction of proarrhythmogenic effect of the V241F KCNQ1 mutation in human atrium. Progress in Biophysics and Molecular Biology. 116(1). 70–75. 11 indexed citations
15.
Nam, Kyoung Won, Ki Moo Lim, Eun Bo Shim, et al.. (2014). Effect of counter-pulsation control of a pulsatile left ventricular assist device on working load variations of the native heart. BioMedical Engineering OnLine. 13(1). 35–35. 10 indexed citations
16.
Lim, Ki Moo, et al.. (2011). Numerical simulation of motion-induced dynamic noise in a ubiquitous ECG application. PubMed. 15. 997–1000. 1 indexed citations
17.
Lim, Ki Moo, Jason Constantino, Viatcheslav Gurev, et al.. (2011). Comparison of the effects of continuous and pulsatile left ventricular-assist devices on ventricular unloading using a cardiac electromechanics model. The Journal of Physiological Sciences. 62(1). 11–19. 29 indexed citations
18.
Shim, Eun Bo, et al.. (2010). Integrative Solution of Heart Ischemia from Ion Channels of Cell to 3D Coronary Blood Flow. 한국전산유체공학회 학술대회논문집. 315–316. 2 indexed citations
19.
Lim, Ki Moo, Ji Hyun Kim, & Eun Bo Shim. (2009). Mathematical Analysis of the Long-Term Efficacy of Daily Home Hemodialysis Therapy with a Cold Dialysate Regeneration System. Blood Purification. 29(1). 27–34. 3 indexed citations
20.
Lee, Jung Chan, Jeong Chul Kim, Ki Moo Lim, et al.. (2008). Comparison of a Pulsatile Blood Pump and a Peristaltic Roller Pump During Hemoperfusion Treatment in a Canine Model of Paraquat Poisoning. Artificial Organs. 32(7). 541–546. 5 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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