M. Todd Young
Impact in
- Health Information Management top 10%
- Artificial Intelligence in Healthcare
Papers in
-
- Machine Learning in Healthcare 2
- Topic Modeling 2
-
- Protein Structure and Dynamics 2
- Co-authors
- Shang Gao (3 shared papers)Arvind Ramanathan (4 shared papers)Hong‐Jun Yoon (3 shared papers)Georgia D. Tourassi (3 shared papers)Jacob Hinkle (5 shared papers)Debsindhu Bhowmik (3 shared papers)John Gounley (3 shared papers)John X. Qiu (1 shared paper)
- Journals
- Journal of Parallel and Distributed Computing (1 paper)Journal of the American Medical Informatics Association (1 paper)Frontiers in Physiology (1 paper)Statistical Science (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
M. Todd Young
12 papers receiving 345 citations
Peers
Comparison fields: 5 of 85
- Health Informatics 9
- Health Information Management 22
- Artificial Intelligence 162
- Computational Theory and Mathematics 24
- Molecular Biology 96
Countries citing papers authored by M. Todd Young
This map shows the geographic impact of M. Todd Young'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 M. Todd Young with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Todd Young more than expected).
Fields of papers citing papers by M. Todd Young
This network shows the impact of papers produced by M. Todd Young. 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 M. Todd Young. The network helps show where M. Todd Young may publish in the future.
Co-authors
The 25 scholars most cited alongside M. Todd Young, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2021 | 107 | |
| 2 | 2017 | 91 | |
| 3 | 2018 | 52 | |
| 4 | 2022 | 31 | |
| 5 | 2020 | 27 | |
| 6 | 1995 | 12 | |
| 7 | 2018 | 11 | |
| 8 | 2019 | 8 | |
| 9 | 2023 | 3 | |
| 10 | 2021 | 3 | |
| 11 | 2021 | 2 | |
| 12 | Towards Exascale Bio-molecular Simulations with Artificial Intelligence Workflows | 2019 | 1 |
| 13 | 2021 | 0 |
About M. Todd Young
M. Todd Young is a scholar working on Artificial Intelligence, Molecular Biology, Pulmonary and Respiratory Medicine, Infectious Diseases and Radiology, Nuclear Medicine and Imaging, having authored 13 papers that have together received 348 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (2 papers), Advanced Bandit Algorithms Research (2 papers), Machine Learning in Healthcare (2 papers), Topic Modeling (2 papers), Protein Structure and Dynamics (2 papers), COVID-19 diagnosis using AI (2 papers), Lung Cancer Diagnosis and Treatment (2 papers) and Respiratory Support and Mechanisms (1 paper). The work is most often cited by research in Health Informatics (9 citations), Health Information Management (22 citations), Artificial Intelligence (162 citations), Computational Theory and Mathematics (24 citations) and Molecular Biology (96 citations). M. Todd Young has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Shang Gao, Arvind Ramanathan, Hong‐Jun Yoon, Georgia D. Tourassi, Jacob Hinkle, Debsindhu Bhowmik, John Gounley, John X. Qiu, Paul Fearn and Ramakrishnan Kannan. Their work appears in journals such as Journal of Parallel and Distributed Computing, Journal of the American Medical Informatics Association, Frontiers in Physiology, Statistical Science and IEEE Journal of Biomedical and Health Informatics.
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.