J Šochman

1.1k citations
62 papers · 865 · h-index 14

Impact in

Papers in

J Šochman

54 papers receiving 804 citations

Peers

J Šochman
Comparison fields: 5 of 109
  • Computer Vision and Pattern Recognition 392
  • Nephrology 48
  • Cardiology and Cardiovascular Medicine 141
  • Automotive Engineering 75
  • Pathology and Forensic Medicine 77
Replace Hongtao Shi with:
Hongtao Shi China
Michiko Watanabe Japan
Hua Chai China
Fei Zuo United States
Hung‐Ju Lin Taiwan
Yuichiro Toda Japan
M Kawade Japan
John J. Sampson United States
J Šochman relative to Hongtao Shi China Hongtao Shi's profile →
Citations per field
00.5×7.7×
Hongtao Shi · 1×
Citations per year

Countries citing papers authored by J Šochman

Since Specialization
Citations

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

Fields of papers citing papers by J Šochman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside J Šochman, 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 J Šochman Line = papers co-authored together J Šochman 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 2005178
2 2012107
3 200284
4 199052
5 199651
6 200046
7 201144
8 201334
9 200424
10 201923
11 199220
12 200717
13 200915
14 200513
15 200813
16 201010
17 20189
18 20109
19
Insertion/deletion polymorphism in the angiotensin-converting enzyme gene in myocardial infarction survivors.
20019
20 20087

About J Šochman

J Šochman is a scholar working on Cardiology and Cardiovascular Medicine, Computer Vision and Pattern Recognition, Surgery, Artificial Intelligence and Pulmonary and Respiratory Medicine, having authored 62 papers that have together received 865 indexed citations. Recurring topics across this work include Cardiac Valve Diseases and Treatments (10 papers), Cardiac Structural Anomalies and Repair (8 papers), Machine Learning and Algorithms (5 papers), Infective Endocarditis Diagnosis and Management (5 papers), Transplantation: Methods and Outcomes (5 papers), Anomaly Detection Techniques and Applications (4 papers), Face and Expression Recognition (4 papers) and Mechanical Circulatory Support Devices (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (392 citations), Nephrology (48 citations), Cardiology and Cardiovascular Medicine (141 citations), Automotive Engineering (75 citations) and Pathology and Forensic Medicine (77 citations). J Šochman has collaborated with scholars based in Czechia, United States and Switzerland. Frequent co-authors include Jiřı́ Matas, J Peregrín, David Hogg, C. Caraffi, Dušan Pavčnik, J Kolc, Josef Rösch, Hans A. Timmermans, Lothar Hotz and Murray Evans. Their work appears in journals such as Physiological Research, CardioVascular and Interventional Radiology, International Journal of Cardiology, Pacing and Clinical Electrophysiology and Clinica Chimica Acta.

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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