Willie Boag
Impact in
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- Artificial Intelligence in Healthcare and Education
- Health Information Management top 10%
- Artificial Intelligence in Healthcare
- Medical Coding and Health Information
Papers in
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- Machine Learning in Healthcare 2
- Natural Language Processing Techniques 1
- Topic Modeling 1
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- Biomedical Text Mining and Ontologies 2
- Co-authors
- Peter Szolovits (3 shared papers)Tristan Naumann (2 shared papers)Matthew B. A. McDermott (2 shared papers)Marzyeh Ghassemi (3 shared papers)Wei‐Hung Weng (1 shared paper)Guanxiong Liu (1 shared paper)Tzu-Ming Harry Hsu (1 shared paper)Michael C. Hughes (1 shared paper)
- Journals
- PubMed (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United States
In The Last Decade
Willie Boag
4 papers receiving 59 citations
Peers
Comparison fields: 5 of 21
- Health Informatics 10
- Health Information Management 14
- Artificial Intelligence 43
- Family Practice 1
- Computer Vision and Pattern Recognition 8
Countries citing papers authored by Willie Boag
This map shows the geographic impact of Willie Boag'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 Willie Boag with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Willie Boag more than expected).
Fields of papers citing papers by Willie Boag
This network shows the impact of papers produced by Willie Boag. 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 Willie Boag. The network helps show where Willie Boag may publish in the future.
Co-authors
The 12 scholars most cited alongside Willie Boag, 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 | What's in a Note? Unpacking Predictive Value in Clinical Note Representations. | 2018 | 37 |
| 2 | Clinically Accurate Chest X-Ray Report Generation. | 2019 | 16 |
| 3 | Feature Robustness in Non-stationary Health Records: Caveats to Deployable Model Performance in Common Clinical Machine Learning Tasks | 2019 | 5 |
| 4 | 2018 | 1 |
About Willie Boag
Willie Boag is a scholar working on Artificial Intelligence, Molecular Biology, Health Information Management, Epidemiology and Public Health, Environmental and Occupational Health, having authored 4 papers that have together received 59 indexed citations. Recurring topics across this work include Biomedical Text Mining and Ontologies (2 papers), Machine Learning in Healthcare (2 papers), Artificial Intelligence in Healthcare (1 paper), Electronic Health Records Systems (1 paper), Natural Language Processing Techniques (1 paper), Palliative Care and End-of-Life Issues (1 paper), Healthcare Policy and Management (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Health Informatics (10 citations), Health Information Management (14 citations), Artificial Intelligence (43 citations), Family Practice (1 citation) and Computer Vision and Pattern Recognition (8 citations). Willie Boag has collaborated with scholars based in United States. Frequent co-authors include Peter Szolovits, Tristan Naumann, Matthew B. A. McDermott, Marzyeh Ghassemi, Wei‐Hung Weng, Guanxiong Liu, Tzu-Ming Harry Hsu, Michael C. Hughes, Anna Goldenberg and Bret Nestor. Their work appears in journals such as PubMed and arXiv (Cornell University).
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.