Casey Ta

40 papers receiving 613 citations

Peers

Casey Ta
Comparison fields: 5 of 104
  • Health Informatics 57
  • Health Information Management 42
  • Artificial Intelligence 189
  • Radiology, Nuclear Medicine and Imaging 103
  • Statistics, Probability and Uncertainty 29
Replace Patrick D. Tyler with:
Patrick D. Tyler United States
S. P. Somashekhar India
Deborah Plana United States
Xun Xiao China
Jacqueline E. Livsey United Kingdom
Poonam Patil India
Aleidy Silva United States
Ryan Goosen Switzerland
Sheela Agarwal United States
Gudrun Zahlmann Germany
Casey Ta relative to Patrick D. Tyler United States Patrick D. Tyler's profile →
Citations per field
00.5×7.3×
Patrick D. Tyler · 1×
Citations per year

Countries citing papers authored by Casey Ta

Since Specialization
Citations

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

Fields of papers citing papers by Casey Ta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201899
2 201268
3 201947
4 201741
5 202035
6 201830
7 202220
8 202417
9 202017
10 201915
11 201915
12
Dependence of intraocular pressure on induced hypotension and posture during surgical anaesthesia.
198015
13 201215
14 201914
15 201414
16 201913
17 201713
18 201213
19 201912
20 196611

About Casey Ta

Casey Ta is a scholar working on Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging, Biomedical Engineering and Genetics, having authored 46 papers that have together received 627 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (12 papers), Machine Learning in Healthcare (11 papers), Topic Modeling (10 papers), Ultrasound and Hyperthermia Applications (6 papers), Ultrasound Imaging and Elastography (5 papers), Photoacoustic and Ultrasonic Imaging (5 papers), Ethics in Clinical Research (4 papers) and Chronic Disease Management Strategies (4 papers). The work is most often cited by research in Health Informatics (57 citations), Health Information Management (42 citations), Artificial Intelligence (189 citations), Radiology, Nuclear Medicine and Imaging (103 citations) and Statistics, Probability and Uncertainty (29 citations). Casey Ta has collaborated with scholars based in United States, South Korea and Singapore. Frequent co-authors include Chunhua Weng, Andrew C. Kummel, Robert F. Mattrey, Ziran Li, Yuko Kono, Tian Kang, Cong Liu, Christopher V. Barback, Chi Yuan and Patrick Ryan. Their work appears in journals such as Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, Journal of Vacuum Science & Technology B Nanotechnology and Microelectronics Materials Processing Measurement and Phenomena, Investigative Radiology and Journal of Plastic Reconstructive & Aesthetic Surgery.

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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