Nils D. Forkert

7.3k citations
242 papers · 4.2k · 1 hit paper · h-index 33

Impact in

Papers in

Nils D. Forkert

235 papers receiving 4.1k citations

Nils D. Forkert's Hit Papers

Machine learning for precision medicine 2020 · 271 citations
2710+2+4Years since publication50100150200250

Peers

Nils D. Forkert
Comparison fields: 5 of 176
  • Health Informatics 112
  • Neurology 816
  • Neurology 401
  • Internal Medicine 174
  • Rehabilitation 303
Replace Kei Yamada with:
Kei Yamada Japan
Birgit Ertl‐Wagner Germany
Hannu J. Aronen Finland
Chul‐Ho Sohn South Korea
Jean‐Pierre Pruvo France
Toshinori Hirai Japan
Beom Joon Kim South Korea
Qi Yang China
Bram Stieltjes Germany
David M. Yousem United States
Nils D. Forkert relative to Kei Yamada Japan Kei Yamada's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nils D. Forkert

Since Specialization
Citations

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

Fields of papers citing papers by Nils D. Forkert

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Nils D. Forkert. 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 Nils D. Forkert. The network helps show where Nils D. Forkert may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 242 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Machine learning for precision medicine
Hit paper breakdown →
2020271
2 2014165
3 2017165
4 2015110
5 2021105
6 2015105
7 2020103
8 2018103
9 201583
10 202083
11 202077
12 202060
13 201457
14 201255
15 202154
16 201547
17 201646
18 201246
19 201745
20 202144

About Nils D. Forkert

Nils D. Forkert is a scholar working on Radiology, Nuclear Medicine and Imaging, Epidemiology, Pulmonary and Respiratory Medicine, Neurology and Computer Vision and Pattern Recognition, having authored 242 papers that have together received 4.2k indexed citations. Recurring topics across this work include Acute Ischemic Stroke Management (69 papers), Cerebrovascular and Carotid Artery Diseases (68 papers), Advanced MRI Techniques and Applications (42 papers), Advanced Neuroimaging Techniques and Applications (40 papers), Medical Image Segmentation Techniques (24 papers), Functional Brain Connectivity Studies (23 papers), Dementia and Cognitive Impairment Research (19 papers) and Intracranial Aneurysms: Treatment and Complications (17 papers). The work is most often cited by research in Health Informatics (112 citations), Neurology (816 citations), Neurology (401 citations), Internal Medicine (174 citations) and Rehabilitation (303 citations). Nils D. Forkert has collaborated with scholars based in Canada, Germany and United States. Frequent co-authors include Jens Fiehler, Sarah J. MacEachern, Götz Thomalla, Pauline Mouchès, Zahinoor Ismail, Matthias Wilms, Heinz Handels, Deepthi Rajashekar, Eric E. Smith and Bastian Cheng. Their work appears in journals such as Clinical Neuroradiology, American Journal of Neuroradiology, Frontiers in Neurology, PLoS ONE and Scientific Reports.

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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