M. Todd Young
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 10%
- Topic Modeling
- Machine Learning in Healthcare
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
Papers in
-
- Machine Learning in Healthcare 3
- Topic Modeling 3
-
- Advanced Bandit Algorithms Research 2
- Co-authors
- Shang GaoGeorgia D. TourassiArvind RamanathanHong‐Jun YoonJacob HinkleDebsindhu BhowmikJohn GounleyPaul Fearn
- Journals
- IEEE Journal of Biomedical and Health Informatics (1 paper)BMC Bioinformatics (1 paper)Frontiers in Physiology (1 paper)Journal of the American Medical Informatics Association (1 paper)Journal of Parallel and Distributed Computing (1 paper)
- Partner nations
- United StatesUnited Kingdom
In The Last Decade
M. Todd Young
12 papers receiving 330 citations
Peers
Comparison fields: 5 of 88
- Health Informatics 17
- Artificial Intelligence 197
- Health Information Management 28
- Molecular Biology 110
- Issues, ethics and legal aspects 2
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-authorship network
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 | 2023 | 2 | |
| 2 | 2022 | 28 | |
| 3 | 2021 | 3 | |
| 4 | 2021 | 104 | |
| 5 | 2021 | 0 | |
| 6 | 2021 | 2 | |
| 7 | 2020 | 26 | |
| 8 | Towards Exascale Bio-molecular Simulations with Artificial Intelligence Workflows | 2019 | 1 |
| 9 | 2019 | 7 | |
| 10 | 2018 | 50 | |
| 11 | 2018 | 11 | |
| 12 | 2017 | 89 | |
| 13 | 1995 | 12 |
About M. Todd Young
M. Todd Young is a scholar working on Artificial Intelligence, Management Science and Operations Research, Statistics and Probability, Infectious Diseases and Pulmonary and Respiratory Medicine, having authored 13 papers that have together received 335 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (3 papers), Topic Modeling (3 papers), Protein Structure and Dynamics (2 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Biomedical Text Mining and Ontologies (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), COVID-19 diagnosis using AI (2 papers) and Advanced Bandit Algorithms Research (2 papers). The work is most often cited by research in Health Informatics (17 citations), Artificial Intelligence (197 citations), Health Information Management (28 citations), Molecular Biology (110 citations) and Issues, ethics and legal aspects (2 citations). M. Todd Young has collaborated with scholars based in United States and United Kingdom. Frequent co-authors include Shang Gao, Georgia D. Tourassi, Arvind Ramanathan, Hong‐Jun Yoon, Jacob Hinkle, Debsindhu Bhowmik, John Gounley, Paul Fearn, John X. Qiu and Eric B. Durbin. Their work appears in journals such as IEEE Journal of Biomedical and Health Informatics, BMC Bioinformatics, Frontiers in Physiology, Journal of the American Medical Informatics Association and Journal of Parallel and Distributed Computing.
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.