Mats Holmström

2.8k citations
57 papers · 1.9k indexed · 1 hit paper · h-index 23

Mats Holmström

54 papers receiving 1.8k citations

Hit Papers

Artificial intelligence and machine learning for medical ...236202120262022202450100150200

Peers

Mats Holmström
Comparison fields: 5 of 141
  • Otorhinolaryngology 556
  • Immunology and Allergy 589
  • Health Informatics 60
  • Sensory Systems 197
  • Physiology 505
Replace Stephen R. Ell with:
Stephen R. Ell United Kingdom
Gaetano Motta Italy
Markus Brunner Austria
Thompson United Kingdom
Christoph Arens Germany
Koichi Yamaguchi Japan
Seung Won Lee South Korea
Paul Germonpré Belgium
Wei Qian China
Mats Holmström relative to Stephen R. Ell United Kingdom Stephen R. Ell's profile →
Citations per field
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Citations per year

Countries citing papers authored by Mats Holmström

Since Specialization
Citations

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

Fields of papers citing papers by Mats Holmström

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

The 25 scholars most cited alongside Mats Holmström, 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 Mats Holmström Line = papers co-authored together Mats Holmström links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202411
2 202319
3 20231
4 20234
5 202248
6
Artificial intelligence and machine learning for medical imaging: A technology reviewbreakdown →
2021236
7 201960
8 20173
9 201415
10 201318
11 201035
12 20088
13 2005271
14 199648
15 199524
16 199226
17 1991128
18 199122
19 19896
20 198864

About Mats Holmström

Mats Holmström is a scholar working on Immunology and Allergy, Otorhinolaryngology and Sensory Systems, having authored 57 papers that have together received 1.9k indexed citations. Recurring topics across this work include Allergic Rhinitis and Sensitization (20 papers), Nasal Surgery and Airway Studies (14 papers), Indoor Air Quality and Microbial Exposure (11 papers), Olfactory and Sensory Function Studies (10 papers), Sinusitis and nasal conditions (9 papers), Asthma and respiratory diseases (8 papers), Occupational exposure and asthma (7 papers) and Obstructive Sleep Apnea Research (5 papers). The work is most often cited by research in Otorhinolaryngology (556 citations), Immunology and Allergy (589 citations) and Health Informatics (60 citations). Mats Holmström has collaborated with scholars based in Sweden, United Kingdom and Belgium. Frequent co-authors include Glenis Scadding, Bo Wilhelmsson, Valerie Lund, Y. Darby, Valerie J. Lund, V J Lund, Edmond Sterpin, Ana María Barragán Montero, Kevin Souris and Pontus Stierna. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of Allergy and Clinical Immunology and SLEEP.

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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