Peter Schulam
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
-
- Music and Audio Processing 5
- Speech and Audio Processing 3
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- Machine Learning in Healthcare 5
- Natural Language Processing Techniques 3
- Advanced Text Analysis Techniques 2
- Topic Modeling 2
- Co-authors
- Marzyeh Ghassemi (2 shared papers)Andrew L. Beam (2 shared papers)Rajesh Ranganath (2 shared papers)Tristan Naumann (2 shared papers)Irene Y. Chen (2 shared papers)Suchi Saria (5 shared papers)Fredrick M. Wigley (1 shared paper)Florian Metze (5 shared papers)
- Journals
- Arthritis Research & Therapy (1 paper)Journal of Machine Learning Research (1 paper)The Lancet Digital Health (1 paper)Language Resources and Evaluation (1 paper)Annals of Internal Medicine (1 paper)
- Partner nations
- United StatesFinlandCanada
In The Last Decade
Peter Schulam
17 papers receiving 545 citations
Peers
Comparison fields: 5 of 108
- Health Informatics 114
- Health Information Management 79
- Signal Processing 89
- Artificial Intelligence 222
- Family Practice 10
Countries citing papers authored by Peter Schulam
This map shows the geographic impact of Peter Schulam'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 Peter Schulam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Schulam more than expected).
Fields of papers citing papers by Peter Schulam
This network shows the impact of papers produced by Peter Schulam. 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 Peter Schulam. The network helps show where Peter Schulam may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter Schulam, 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 | 2018 | 189 | |
| 2 | 2019 | 96 | |
| 3 | 2020 | 78 | |
| 4 | 2015 | 66 | |
| 5 | 2012 | 46 | |
| 6 | 2012 | 46 | |
| 7 | 2013 | 20 | |
| 8 | 2013 | 18 | |
| 9 | 2018 | 12 | |
| 10 | Integrative analysis using coupled latent variable models for individualizing prognoses | 2016 | 8 |
| 11 | 2018 | 7 | |
| 12 | Automatically Determining the Semantic Gradation of German Adjectives. | 2010 | 5 |
| 13 | Generating Natural Language Summaries for Multimedia | 2012 | 4 |
| 14 | Active Learning for Decision-Making from Imbalanced Observational Data | 2019 | 3 |
| 15 | 2023 | 3 | |
| 16 | 2021 | 1 | |
| 17 | 2018 | 1 | |
| 18 | 2014 | 1 | |
| 19 | 2018 | 0 |
About Peter Schulam
Peter Schulam is a scholar working on Signal Processing, Artificial Intelligence, Health Information Management, Computer Vision and Pattern Recognition and Health Informatics, having authored 19 papers that have together received 604 indexed citations. Recurring topics across this work include Video Analysis and Summarization (5 papers), Music and Audio Processing (5 papers), Machine Learning in Healthcare (5 papers), Artificial Intelligence in Healthcare (3 papers), Natural Language Processing Techniques (3 papers), Speech and Audio Processing (3 papers), Advanced Text Analysis Techniques (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Health Informatics (114 citations), Health Information Management (79 citations), Signal Processing (89 citations), Artificial Intelligence (222 citations) and Family Practice (10 citations). Peter Schulam has collaborated with scholars based in United States, Finland and Canada. Frequent co-authors include Marzyeh Ghassemi, Andrew L. Beam, Rajesh Ranganath, Tristan Naumann, Irene Y. Chen, Suchi Saria, Fredrick M. Wigley, Florian Metze, Susanne Burger and Duo Ding. Their work appears in journals such as Arthritis Research & Therapy, Journal of Machine Learning Research, The Lancet Digital Health, Language Resources and Evaluation and Annals of Internal Medicine.
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.