Min Jhon

55 papers receiving 722 citations

Min Jhon's Hit Papers

A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis 2023 · 133 citations
1330+1+2Years since publication4080120

Peers

Min Jhon
Comparison fields: 5 of 106
  • Biological Psychiatry 56
  • Applied Psychology 65
  • Clinical Psychology 194
  • Psychiatry and Mental health 94
  • Health Informatics 9
Replace Cheryl B. McCullumsmith with:
Cheryl B. McCullumsmith United States
Katrina A. S. Davis United Kingdom
Hyewon Kim South Korea
Ayşe Kurtulmuş Türkiye
Richard Berman United States
Yongbo Zheng China
Qi Zuo China
Mrudula Utukuri United Kingdom
Rashmi Patel United Kingdom
Irene Shyu United States
Min Jhon relative to Cheryl B. McCullumsmith United States Cheryl B. McCullumsmith's profile →
Citations per field
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Cheryl B. McCullumsmith · 1×
Citations per year

Countries citing papers authored by Min Jhon

Since Specialization
Citations

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

Fields of papers citing papers by Min Jhon

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Min Jhon, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Min Jhon Line = papers co-authored together Min Jhon links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 62 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Review of Machine Learning and Deep Learning Approaches on Mental Health Diagnosis
Hit paper breakdown →
2023133
2 2019106
3 201841
4 201938
5 202233
6 202129
7 201626
8 202026
9 202022
10 202020
11 201819
12 202119
13 202018
14 202418
15 202214
16 202114
17 202214
18 201813
19 202212
20 202112

About Min Jhon

Min Jhon is a scholar working on Clinical Psychology, Social Psychology, Psychiatry and Mental health, Biological Psychiatry and Experimental and Cognitive Psychology, having authored 62 papers that have together received 739 indexed citations. Recurring topics across this work include Schizophrenia research and treatment (9 papers), COVID-19 and Mental Health (8 papers), Tryptophan and brain disorders (8 papers), Treatment of Major Depression (7 papers), Mental Health Treatment and Access (5 papers), Mental Health via Writing (4 papers), Digital Mental Health Interventions (4 papers) and Suicide and Self-Harm Studies (4 papers). The work is most often cited by research in Biological Psychiatry (56 citations), Applied Psychology (65 citations), Clinical Psychology (194 citations), Psychiatry and Mental health (94 citations) and Health Informatics (9 citations). Min Jhon has collaborated with scholars based in South Korea, United Kingdom and Australia. Frequent co-authors include Sung‐Wan Kim, Jae‐Min Kim, Ju‐Yeon Lee, Ju‐Wan Kim, Soo-Hyung Kim, Hyung-Jeong Yang, Sudarshan Pant, Seunghyong Ryu, Hee‐Ju Kang and Mina Kim. Their work appears in journals such as Journal of Affective Disorders, Frontiers in Psychiatry, PLoS ONE, Journal of Korean Medical Science and Progress in Neuro-Psychopharmacology and Biological Psychiatry.

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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