Kaleb E Smith

967 citations
5 papers · 471 indexed · 1 hit paper · h-index 3

Impact in

Papers in

Kaleb E Smith

4 papers receiving 454 citations

Hit Papers

A large language model for electronic health records 2022 · 434 citations
4342022202620232024100200300400

Peers

Kaleb E Smith
Comparison fields: 5 of 93
  • Health Informatics 190
  • Health Information Management 69
  • Artificial Intelligence 267
  • Radiology, Nuclear Medicine and Imaging 89
  • Issues, ethics and legal aspects 3
Replace Colin B. Compas with:
Colin B. Compas United States
Yikuan Li United States
Aokun Chen United States
Aniek F. Markus Netherlands
William Wasswa Uganda
Christian Bluethgen Switzerland
Jean-Benoit Delbrouck United States
Wen-wai Yim United States
Joy T. Wu United States
Natalia Norori United Kingdom
Kaleb E Smith relative to Colin B. Compas United States Colin B. Compas's profile →
Citations per field
00.5×1.5×
Colin B. Compas · 1×
Citations per year

Countries citing papers authored by Kaleb E Smith

Since Specialization
Citations

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

Fields of papers citing papers by Kaleb E Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaleb E Smith. 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 Kaleb E Smith. The network helps show where Kaleb E Smith may publish in the future.

Co-authors

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

All Works

5 of 5 papers shown

About Kaleb E Smith

Kaleb E Smith is a scholar working on Artificial Intelligence, Environmental Engineering, Ecology, Infectious Diseases and Organic Chemistry, having authored 5 papers that have together received 471 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Natural Language Processing Techniques (3 papers), Machine Learning in Healthcare (3 papers), Remote Sensing in Agriculture (1 paper), Coastal wetland ecosystem dynamics (1 paper), Text Readability and Simplification (1 paper) and Remote Sensing and LiDAR Applications (1 paper). The work is most often cited by research in Health Informatics (190 citations), Health Information Management (69 citations), Artificial Intelligence (267 citations), Radiology, Nuclear Medicine and Imaging (89 citations) and Issues, ethics and legal aspects (3 citations). Kaleb E Smith has collaborated with scholars based in United States. Frequent co-authors include Xi Yang, Aokun Chen, Yonghui Wu, Jiang Bian, Anthony Costa, Mona G. Flores, Christopher A. Harle, Gloria Lipori, Duane A. Mitchell and Ying Zhang. Their work appears in journals such as Journal of the American Medical Informatics Association, npj Digital Medicine, Journal of Biomedical Informatics and IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.

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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