Seon‐Sook Han

949 total citations
46 papers, 668 citations indexed

About

Seon‐Sook Han is a scholar working on Pulmonary and Respiratory Medicine, Molecular Biology and Physiology. According to data from OpenAlex, Seon‐Sook Han has authored 46 papers receiving a total of 668 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Pulmonary and Respiratory Medicine, 11 papers in Molecular Biology and 9 papers in Physiology. Recurrent topics in Seon‐Sook Han's work include Chronic Obstructive Pulmonary Disease (COPD) Research (9 papers), Asthma and respiratory diseases (7 papers) and Respiratory Support and Mechanisms (4 papers). Seon‐Sook Han is often cited by papers focused on Chronic Obstructive Pulmonary Disease (COPD) Research (9 papers), Asthma and respiratory diseases (7 papers) and Respiratory Support and Mechanisms (4 papers). Seon‐Sook Han collaborates with scholars based in South Korea, United States and Puerto Rico. Seon‐Sook Han's co-authors include Woo Jin Kim, Seung‐Joon Lee, Yoonki Hong, Seok‐Ho Hong, So Hyeon Bak, Sung Ok Kwon, Jeongwon Heo, Jun Yeon Won, Wonjun Ji and Se‐Ran Yang and has published in prestigious journals such as Immunity, PLoS ONE and Scientific Reports.

In The Last Decade

Seon‐Sook Han

40 papers receiving 652 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Seon‐Sook Han South Korea 16 257 155 103 102 71 46 668
Constantinos Glynos Greece 11 276 1.1× 163 1.1× 186 1.8× 66 0.6× 35 0.5× 23 719
Raquel Herrero Spain 17 266 1.0× 290 1.9× 50 0.5× 105 1.0× 61 0.9× 31 760
Yan Yin China 19 301 1.2× 291 1.9× 144 1.4× 87 0.9× 19 0.3× 70 861
Gisele P. Oliveira Brazil 17 329 1.3× 128 0.8× 55 0.5× 74 0.7× 25 0.4× 22 620
Teresa Renda Italy 14 485 1.9× 189 1.2× 260 2.5× 94 0.9× 33 0.5× 24 878
Zhenyu Yang China 18 237 0.9× 380 2.5× 70 0.7× 115 1.1× 59 0.8× 44 887
Amanda Iglesias Spain 15 452 1.8× 148 1.0× 276 2.7× 44 0.4× 60 0.8× 40 760
Chung‐Ching Hua Taiwan 15 107 0.4× 216 1.4× 42 0.4× 75 0.7× 48 0.7× 41 567

Countries citing papers authored by Seon‐Sook Han

Since Specialization
Citations

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

Fields of papers citing papers by Seon‐Sook Han

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Seon‐Sook Han. 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 Seon‐Sook Han. The network helps show where Seon‐Sook Han may publish in the future.

Co-authorship network of co-authors of Seon‐Sook Han

This figure shows the co-authorship network connecting the top 25 collaborators of Seon‐Sook Han. A scholar is included among the top collaborators of Seon‐Sook Han 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 Seon‐Sook Han. Seon‐Sook Han 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.
Park, Jinkyeong, Donghoon Lee, Minkyu Kim, et al.. (2025). DeepTDM: Deep Learning-Based Prediction of Sequential Therapeutic Drug Monitoring Levels of Vancomycin. IEEE Journal of Translational Engineering in Health and Medicine. 13. 493–506.
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Wu, Min, et al.. (2024). CO111 Indirect Treatment Comparisons of Lefamulin Versus Omadacycline for the Treatment of Community-Acquired Pneumonia. Value in Health. 27(12). S35–S35. 1 indexed citations
5.
Choi, Hyun-Soo, et al.. (2023). A Deep Learning–Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness. JMIR Formative Research. 8. e45202–e45202. 6 indexed citations
6.
Jo, Heui Sug, et al.. (2023). Study Protocol for a Hospital-to-Home Transitional Care for Older Adults Hospitalized with Chronic Obstructive Pulmonary Disease in South Korea: A Randomized Controlled Trial. International Journal of Environmental Research and Public Health. 20(15). 6507–6507.
7.
Kim, Minkyu, T. Y. Kim, Jeongwon Heo, et al.. (2023). In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables. Journal of Clinical Medicine. 13(1). 36–36. 4 indexed citations
8.
Han, Seon‐Sook, et al.. (2020). Effects of age on the association between pulmonary tuberculosis and lung cancer in a South Korean cohort. Journal of Thoracic Disease. 12(3). 375–382. 21 indexed citations
9.
Bak, So Hyeon, Sung Ok Kwon, Seon‐Sook Han, & Woo Jin Kim. (2019). Computed tomography-derived area and density of pectoralis muscle associated disease severity and longitudinal changes in chronic obstructive pulmonary disease: a case control study. Respiratory Research. 20(1). 226–226. 52 indexed citations
10.
Han, Seon‐Sook, et al.. (2019). Evaluation and treatment of latent tuberculosis infection among healthcare workers in Korea: A multicentre cohort analysis. PLoS ONE. 14(9). e0222810–e0222810. 12 indexed citations
11.
Kim, Woo Jin, Yoonki Hong, Seung‐Joon Lee, et al.. (2018). Plasma CRABP2 as a Novel Biomarker in Patients with Non-Small Cell Lung Cancer. Journal of Korean Medical Science. 33(26). e178–e178. 13 indexed citations
12.
Heo, Jeongwon, Yoonki Hong, Seon‐Sook Han, et al.. (2018). Changes in the Characteristics and Long-term Mortality Rates of Intensive Care Unit Patients from 2003 to 2010: A Nationwide Population-Based Cohort Study Performed in the Republic of Korea. Acute and Critical Care. 33(3). 135–145. 10 indexed citations
13.
Moon, Hyung‐Geun, Seung-Jae Kim, Seon‐Sook Han, et al.. (2018). Airway Epithelial Cell-Derived Colony Stimulating Factor-1 Promotes Allergen Sensitization. Immunity. 49(2). 275–287.e5. 59 indexed citations
14.
Hong, Yoonki, et al.. (2017). A cluster analysis of chronic obstructive pulmonary disease in dusty areas cohort identified three subgroups. BMC Pulmonary Medicine. 17(1). 209–209. 20 indexed citations
15.
Kim, Jeeyoung, Haengseok Song, Hye Ryun Kim, et al.. (2017). Cadmium-induced ER stress and inflammation are mediated through C/EBP–DDIT3 signaling in human bronchial epithelial cells. Experimental & Molecular Medicine. 49(9). e372–e372. 45 indexed citations
16.
Han, Seon‐Sook, et al.. (2016). Serum microRNAs as potential biomarkers for lung cancer. Annals of Oncology. 27. vi529–vi529. 2 indexed citations
17.
Han, Seon‐Sook, Won Ho Lee, Yoonki Hong, et al.. (2016). Comparison of serum biomarkers between patients with asthma and with chronic obstructive pulmonary disease. Journal of Asthma. 53(6). 583–588. 12 indexed citations
18.
Hong, Yoonki, et al.. (2016). Sex differences of COPD phenotypes in nonsmoking patients. International Journal of COPD. Volume 11. 1657–1662. 30 indexed citations
19.
Lee, Won‐Ho, et al.. (2013). Very Early Onset of Amiodarone-Induced Pulmonary Toxicity. Korean Circulation Journal. 43(10). 699–699. 7 indexed citations
20.
Han, Seon‐Sook, Woo Jin Kim, Seung‐Joon Lee, Byung Ryul Cho, & Sung Won Lee. (2004). A case of eosinophilic pleural effusion who was serologically positive for both Paragonimus westermani and Toxocara canis.. The Korean Journal of Internal Medicine. 67(6). 650–654. 1 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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