Inbar Fried

16 papers receiving 332 citations

Peers

Inbar Fried
Comparison fields: 5 of 89
  • Obstetrics and Gynecology 42
  • Pediatrics, Perinatology and Child Health 103
  • Health Informatics 7
  • Medical Laboratory Technology 6
  • Pulmonary and Respiratory Medicine 67
Replace Dagmar de Bruijn with:
Dagmar de Bruijn Netherlands
Shyamala Guruvare India
Ross Upton United Kingdom
Maxwell Emerson United States
Armando Cuttano Italy
Trish Chudleigh United Kingdom
Elisenda Bonet-Carné Spain
Wen Zhong China
Yimei Liao China
Komsun Suwannarurk Thailand
Inbar Fried relative to Dagmar de Bruijn Netherlands Dagmar de Bruijn's profile →
Citations per field
00.5×
Dagmar de Bruijn · 1×
Citations per year

Countries citing papers authored by Inbar Fried

Since Specialization
Citations

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

Fields of papers citing papers by Inbar Fried

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

17 of 17 papers shown
#Work
1 2020116
2 201933
3 201530
4 202329
5 202124
6 201723
7 201921
8
Clinical Concept Embeddings Learned from Massive Sources of Medical Data.
201817
9 202014
10 20206
11 20176
12 20215
13 20235
14 20224
15 20202
16 20251
17 20230

About Inbar Fried

Inbar Fried is a scholar working on Biomedical Engineering, Computer Vision and Pattern Recognition, Aerospace Engineering, Surgery and Molecular Biology, having authored 17 papers that have together received 336 indexed citations. Recurring topics across this work include Soft Robotics and Applications (7 papers), Robotics and Sensor-Based Localization (5 papers), Robotic Path Planning Algorithms (3 papers), Computational Drug Discovery Methods (2 papers), Birth, Development, and Health (2 papers), Surgical Simulation and Training (2 papers), Child and Adolescent Health (2 papers) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Obstetrics and Gynecology (42 citations), Pediatrics, Perinatology and Child Health (103 citations), Health Informatics (7 citations), Medical Laboratory Technology (6 citations) and Pulmonary and Respiratory Medicine (67 citations). Inbar Fried has collaborated with scholars based in United States, Australia and Israel. Frequent co-authors include Isaac S. Kohane, Andrew L. Beam, Nathan Palmer, Anna D. Sinaiko, Joanne Armstrong, Gabriel A. Brat, Denis Agniel, John A. F. Zupancic, Kathe Fox and Jason Akulian. Their work appears in journals such as PLoS ONE, Science Robotics, BJOG An International Journal of Obstetrics & Gynaecology, American Journal of Obstetrics and Gynecology and IEEE Transactions on Robotics.

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.

Explore authors with similar magnitude of impact