Jae Min

991 citations
24 papers · 547 indexed · 1 hit paper · h-index 10
Topics
Antibiotics Pharmacokinetics and Efficacy (3 papers)Digital Mental Health Interventions (3 papers)Mental Health Research Topics (3 papers)

In The Last Decade

Jae Min

22 papers receiving 536 citations

Hit Papers

Causal inference and counterfactual prediction in machine...2020202620222024202050100150200

Peers

Jae Min
Comparison fields: 5 of 134
  • Artificial Intelligence 134
  • Public Health, Environmental and Occupational Health 64
  • Epidemiology 60
  • Health Informatics 57
  • Radiology, Nuclear Medicine and Imaging 54
Replace Carolina Riveros with:
Carolina Riveros France
Rui Duan United States
Yiye Zhang United States
Rupa Makadia United States
Danielle L. Mowery United States
Jeremy C. Weiss United States
Fuchiang Tsui United States
Adam Hanina United States
Shannan N. Rich United States
Bilal A. Mateen United Kingdom
Jae Min relative to Carolina Riveros France Carolina Riveros's profile →
Citations per field
00.5×3.4×
Carolina Riveros · 1×
Citations per year

Countries citing papers authored by Jae Min

Since Specialization
Citations

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

Fields of papers citing papers by Jae Min

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jae Min

This figure shows the co-authorship network connecting the top 25 collaborators of Jae Min. A scholar is included among the top collaborators of Jae Min 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 Jae Min. Jae Min 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
#WorkIndexed citations
1 0
2 2
3 3
4 5
5 10
6 4
7 5
8 20
9 47
10
Causal inference and counterfactual prediction in machine learning for actionable healthcarebreakdown →
219
11 3
12 2
13 12
14 1
15 8
16 112
17 25
18 29
19 7
20 15

About Jae Min

Jae Min is a scholar working on Applied Psychology, Health Information Management and Molecular Medicine, having authored 24 papers that have together received 547 indexed citations. Recurring topics across this work include Antibiotics Pharmacokinetics and Efficacy (3 papers), Digital Mental Health Interventions (3 papers) and Mental Health Research Topics (3 papers). The work is most often cited by research in Health Informatics (57 citations), Health Information Management (45 citations) and Toxicology (20 citations). Jae Min has collaborated with scholars based in United States, Italy and United Kingdom. Frequent co-authors include Mattia Prosperi, Jiang Bian, François Modave, Mo Wang, Matthew Sperrin, James S. Koopman, Xing He, Shannan N. Rich, Yi Guo and Iain Buchan. Their work appears in journals such as PLoS ONE, Scientific Reports and American Journal of Public Health.

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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