Suehyun Lee

994 total citations · 1 hit paper
47 papers, 603 citations indexed

About

Suehyun Lee is a scholar working on Toxicology, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Suehyun Lee has authored 47 papers receiving a total of 603 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Toxicology, 10 papers in Computational Theory and Mathematics and 6 papers in Molecular Biology. Recurrent topics in Suehyun Lee's work include Pharmacovigilance and Adverse Drug Reactions (15 papers), Computational Drug Discovery Methods (10 papers) and Pharmaceutical Practices and Patient Outcomes (6 papers). Suehyun Lee is often cited by papers focused on Pharmacovigilance and Adverse Drug Reactions (15 papers), Computational Drug Discovery Methods (10 papers) and Pharmaceutical Practices and Patient Outcomes (6 papers). Suehyun Lee collaborates with scholars based in South Korea, United States and Puerto Rico. Suehyun Lee's co-authors include Hun‐Sung Kim, Ju Han Kim, Jong‐Yeup Kim, Kye Hwa Lee, Yu Rang Park, Dong‐Kyu Kim, Kwangsoo Kim, Hyunjeong Cho, Seokwoo Park and Ho Jun Chin and has published in prestigious journals such as PLoS ONE, Scientific Reports and Journal of the American Society of Nephrology.

In The Last Decade

Suehyun Lee

45 papers receiving 588 citations

Hit Papers

Real-world Evidence versus Randomized Controlled Trial: C... 2018 2026 2020 2023 2018 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Suehyun Lee South Korea 11 86 73 63 61 61 47 603
Lamiae Grimaldi‐Bensouda France 20 155 1.8× 79 1.1× 49 0.8× 99 1.6× 124 2.0× 64 1.2k
Man Young Park South Korea 12 49 0.6× 39 0.5× 60 1.0× 31 0.5× 52 0.9× 30 527
Seok‐Jae Heo South Korea 15 80 0.9× 59 0.8× 30 0.5× 28 0.5× 70 1.1× 78 658
Ylenia Ingrasciotta Italy 17 62 0.7× 70 1.0× 103 1.6× 97 1.6× 21 0.3× 44 789
Giuseppe Roberto Italy 15 186 2.2× 64 0.9× 108 1.7× 12 0.2× 108 1.8× 50 726
Alan M. Hochberg United States 10 111 1.3× 26 0.4× 168 2.7× 34 0.6× 38 0.6× 18 611
Grégoire Ficheur France 15 135 1.6× 71 1.0× 60 1.0× 14 0.2× 47 0.8× 59 559
Ioana Danciu United States 12 132 1.5× 105 1.4× 23 0.4× 338 5.5× 70 1.1× 24 998
Frank DeFalco United States 12 161 1.9× 72 1.0× 20 0.3× 17 0.3× 55 0.9× 24 934
Vivienne J. Zhu United States 13 76 0.9× 74 1.0× 9 0.1× 91 1.5× 33 0.5× 24 784

Countries citing papers authored by Suehyun Lee

Since Specialization
Citations

This map shows the geographic impact of Suehyun Lee'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 Suehyun Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Suehyun Lee more than expected).

Fields of papers citing papers by Suehyun Lee

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Suehyun Lee. 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 Suehyun Lee. The network helps show where Suehyun Lee may publish in the future.

Co-authorship network of co-authors of Suehyun Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Suehyun Lee. A scholar is included among the top collaborators of Suehyun Lee 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 Suehyun Lee. Suehyun Lee 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.
Lee, Suehyun, et al.. (2025). Challenges for Data Quality in the Clinical Data Life Cycle: Systematic Review. Journal of Medical Internet Research. 27. e60709–e60709. 2 indexed citations
2.
Lee, Jeong Hoon, et al.. (2025). Predicting Nottingham grade in breast cancer digital pathology using a foundation model. Breast Cancer Research. 27(1). 58–58. 1 indexed citations
3.
Kim, Jong‐Yeup, et al.. (2024). SNSMiner_VAC: Analyzing vaccination based on social network service data for safety surveillance. Expert Systems with Applications. 255. 124684–124684. 1 indexed citations
5.
6.
Kim, Jong‐Yeup, et al.. (2023). Effective data quality management for electronic medical record data using SMART DATA. International Journal of Medical Informatics. 180. 105262–105262. 3 indexed citations
7.
Han, Ae Ra, Suehyun Lee, Suehyun Lee, et al.. (2023). Genital tract infection and pelvic surgery contribute to the development of endometriosis. Journal of Reproductive Immunology. 156. 103831–103831. 1 indexed citations
8.
Lee, Suehyun, et al.. (2022). Adverse Drug Reaction Analysis Methods and Research Trends by Data Sources for Post-marketing Surveillance. Journal of Health Informatics and Statistics. 47(Suppl 3). S41–S50. 1 indexed citations
9.
Lee, Suehyun, et al.. (2022). A Data-Driven Reference Standard for Adverse Drug Reaction (RS-ADR) Signal Assessment: Development and Validation. Journal of Medical Internet Research. 24(10). e35464–e35464. 1 indexed citations
10.
Choi, Jihye, Jong‐Yeup Kim, Seong Uk Kwon, et al.. (2022). Time to surgery and survival in breast cancer. BMC Surgery. 22(1). 388–388. 11 indexed citations
11.
Lee, Suehyun & Hun‐Sung Kim. (2021). Prospect of Artificial Intelligence Based on Electronic Medical Records. Journal of Lipid and Atherosclerosis. 10(3). 282–282. 33 indexed citations
12.
Kim, Sun Moon, Suehyun Lee, Jee-Young Hong, et al.. (2021). Effect of Ranitidine Intake on the Risk of Gastric Cancer Development. Healthcare. 9(8). 1071–1071. 7 indexed citations
13.
Kim, Hyunah, et al.. (2021). Angiotensin‐converting enzyme inhibitors versus angiotensin receptor blockers: New‐onset diabetes mellitus stratified by statin use. Journal of Clinical Pharmacy and Therapeutics. 47(1). 97–103. 1 indexed citations
14.
Lee, Suehyun, et al.. (2021). An OMOP-CDM based pharmacovigilance data-processing pipeline (PDP) providing active surveillance for ADR signal detection from real-world data sources. BMC Medical Informatics and Decision Making. 21(1). 159–159. 7 indexed citations
15.
Lee, Seung Mi, Young Ju Kim, Han Sung Hwang, et al.. (2020). Identifying genetic variants associated with ritodrine-induced pulmonary edema. PLoS ONE. 15(11). e0241215–e0241215. 2 indexed citations
16.
Park, Yu Rang, et al.. (2020). Composite CDE: modeling composite relationships between common data elements for representing complex clinical data. BMC Medical Informatics and Decision Making. 20(1). 147–147. 8 indexed citations
17.
Oh, Sanghoon, Tae Young Lee, Minah Kim, et al.. (2020). Effectiveness of antipsychotic drugs in schizophrenia: a 10-year retrospective study in a Korean tertiary hospital. Schizophrenia. 6(1). 32–32. 10 indexed citations
18.
Lee, Suehyun, Rae Woong Park, John Hoon Rim, et al.. (2019). Development of a Controlled Vocabulary-Based Adverse Drug Reaction Signal Dictionary for Multicenter Electronic Health Record-Based Pharmacovigilance. Drug Safety. 42(5). 657–670. 11 indexed citations
19.
Kim, Hun‐Sung, Suehyun Lee, & Ju Han Kim. (2018). Real-world Evidence versus Randomized Controlled Trial: Clinical Research Based on Electronic Medical Records. Journal of Korean Medical Science. 33(34). e213–e213. 269 indexed citations breakdown →
20.
Hong, Chang-Won, Young Mo Kim, Hongryull Pyo, et al.. (2013). Involvement of inducible nitric oxide synthase in radiation-induced vascular endothelial damage. Journal of Radiation Research. 54(6). 1036–1042. 22 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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