Peter Schulam
Impact in
- Health Informatics top 1%
- Artificial Intelligence in Healthcare and Education
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- Artificial Intelligence in Healthcare
Papers in
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- Artificial Intelligence in Healthcare 3
- Co-authors
- Suchi SariaAndrew L. BeamTristan NaumannMarzyeh GhassemiRajesh RanganathIrene Y. ChenFredrick M. WigleyFlorian Metze
- Journals
- Annals of Internal Medicine (1 paper)The Lancet Digital Health (1 paper)Journal of Machine Learning Research (1 paper)Arthritis Research & Therapy (1 paper)Language Resources and Evaluation (1 paper)
- Partner nations
- United StatesCanadaFinland
In The Last Decade
Peter Schulam
20 papers receiving 461 citations
Peers
Comparison fields: 5 of 105
- Health Informatics 115
- Health Information Management 75
- Signal Processing 82
- Family Practice 16
- Artificial Intelligence 219
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-authorship network
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 | 2021 | 1 | |
| 2 | 2020 | 60 | |
| 3 | 2019 | 52 | |
| 4 | Can You Trust This Prediction? Auditing Pointwise Reliability After Learning | 2019 | 10 |
| 5 | Active Learning for Decision-Making from Imbalanced Observational Data | 2019 | 2 |
| 6 | Learning Predictive Models That Transport. | 2018 | 1 |
| 7 | 2018 | 0 | |
| 8 | 2018 | 6 | |
| 9 | 2018 | 11 | |
| 10 | 2018 | 1 | |
| 11 | What-If Reasoning with Counterfactual Gaussian Processes | 2017 | 1 |
| 12 | What-If Reasoning using Counterfactual Gaussian Processes | 2017 | 1 |
| 13 | Integrative analysis using coupled latent variable models for individualizing prognoses | 2016 | 6 |
| 14 | 2015 | 61 | |
| 15 | 2014 | 1 | |
| 16 | 2013 | 14 | |
| 17 | 2013 | 18 | |
| 18 | Generating Natural Language Summaries for Multimedia | 2012 | 2 |
| 19 | 2012 | 42 | |
| 20 | Automatically Determining the Semantic Gradation of German Adjectives. | 2010 | 5 |
About Peter Schulam
Peter Schulam is a scholar working on Health Informatics, Health Information Management, Artificial Intelligence, Signal Processing and Computer Vision and Pattern Recognition, having authored 23 papers that have together received 496 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (6 papers), Video Analysis and Summarization (5 papers), Music and Audio Processing (5 papers), Natural Language Processing Techniques (4 papers), Speech and Audio Processing (3 papers), Topic Modeling (3 papers), Artificial Intelligence in Healthcare (3 papers) and Bayesian Modeling and Causal Inference (2 papers). The work is most often cited by research in Health Informatics (115 citations), Health Information Management (75 citations), Signal Processing (82 citations), Family Practice (16 citations) and Artificial Intelligence (219 citations). Peter Schulam has collaborated with scholars based in United States, Canada and Finland. Frequent co-authors include Suchi Saria, Andrew L. Beam, Tristan Naumann, Marzyeh Ghassemi, Rajesh Ranganath, Irene Y. Chen, Fredrick M. Wigley, Florian Metze, Susanne Burger and Duo Ding. Their work appears in journals such as Annals of Internal Medicine, The Lancet Digital Health, Journal of Machine Learning Research, Arthritis Research & Therapy and Language Resources and Evaluation.
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.