William Boag

1.8k total citations · 1 hit paper
12 papers, 873 citations indexed

About

William Boag is a scholar working on Artificial Intelligence, Molecular Biology and Health Information Management. According to data from OpenAlex, William Boag has authored 12 papers receiving a total of 873 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Health Information Management. Recurrent topics in William Boag's work include Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Machine Learning in Healthcare (2 papers). William Boag is often cited by papers focused on Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Machine Learning in Healthcare (2 papers). William Boag collaborates with scholars based in United States, United Kingdom and Pakistan. William Boag's co-authors include Matthew B. A. McDermott, Tristan Naumann, Emily Alsentzer, Wei‐Hung Weng, John R. Murphy, Anna Rumshisky, Peter Szolovits, Catherine D’Ignazio, Harini Suresh and Roy H. Perlis and has published in prestigious journals such as Translational Psychiatry, npj Digital Medicine and Text REtrieval Conference.

In The Last Decade

William Boag

12 papers receiving 836 citations

Hit Papers

Publicly Available Clinical 2019 2026 2021 2023 2019 250 500 750

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
William Boag United States 6 715 301 99 93 83 12 873
Emily Alsentzer United States 9 827 1.2× 333 1.1× 255 2.6× 109 1.2× 134 1.6× 22 1.2k
Saeed Mehrabi United States 9 517 0.7× 337 1.1× 93 0.9× 129 1.4× 90 1.1× 15 796
Robert Tinn United States 4 840 1.2× 483 1.6× 166 1.7× 48 0.5× 105 1.3× 5 1.1k
Chaitanya Shivade United States 11 588 0.8× 297 1.0× 54 0.5× 141 1.5× 42 0.5× 20 817
Michael Lucas Australia 4 790 1.1× 456 1.5× 146 1.5× 44 0.5× 91 1.1× 14 1.1k
Aurélie Névéol France 19 944 1.3× 809 2.7× 75 0.8× 79 0.8× 71 0.9× 71 1.4k
Hans Moen Finland 11 464 0.6× 333 1.1× 137 1.4× 78 0.8× 46 0.6× 50 824
Meliha Yetisgen-Yildiz United States 15 498 0.7× 468 1.6× 36 0.4× 58 0.6× 70 0.8× 29 765
Philipp Daumke Germany 9 377 0.5× 331 1.1× 25 0.3× 126 1.4× 54 0.7× 29 696
Amber Stubbs United States 13 824 1.2× 471 1.6× 64 0.6× 163 1.8× 23 0.3× 19 978

Countries citing papers authored by William Boag

Since Specialization
Citations

This map shows the geographic impact of William 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 William Boag with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Boag more than expected).

Fields of papers citing papers by William Boag

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William 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 William Boag. The network helps show where William Boag may publish in the future.

Co-authorship network of co-authors of William Boag

This figure shows the co-authorship network connecting the top 25 collaborators of William Boag. A scholar is included among the top collaborators of William Boag based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with William Boag. William Boag is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

12 of 12 papers shown
1.
Boag, William, Alifia Hasan, Marshall Nichols, et al.. (2024). The algorithm journey map: a tangible approach to implementing AI solutions in healthcare. npj Digital Medicine. 7(1). 87–87. 6 indexed citations
2.
Boag, William, Alifia Hasan, Henry David Jeffry Hogg, et al.. (2023). Organizational Governance of Emerging Technologies: AI Adoption in Healthcare. 1396–1417. 19 indexed citations
3.
Boag, William, et al.. (2022). Tech Worker Organizing for Power and Accountability. 452–463. 17 indexed citations
4.
Boag, William, et al.. (2021). Hard for humans, hard for machines: predicting readmission after psychiatric hospitalization using narrative notes. Translational Psychiatry. 11(1). 32–32. 19 indexed citations
5.
Boag, William, et al.. (2021). A Pilot Study in Surveying Clinical Judgments to Evaluate Radiology Report Generation. 269. 458–465. 2 indexed citations
6.
Boag, William, et al.. (2020). Clinical Collabsheets: 53 Questions to Guide a Clinical Collaboration.. 783–812. 3 indexed citations
7.
Alsentzer, Emily, John R. Murphy, William Boag, et al.. (2019). Publicly Available Clinical. 72–78. 781 indexed citations breakdown →
8.
Boag, William, et al.. (2019). Baselines for Chest X-Ray Report Generation. 126–140. 13 indexed citations
9.
Yuan, Ling, Sadid A. Hasan, Michele Filannino, et al.. (2017). A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching.. Text REtrieval Conference. 2 indexed citations
10.
Potash, Peter, William Boag, Alexey Romanov, Vasili Ramanishka, & Anna Rumshisky. (2016). SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity. 741–748. 4 indexed citations
11.
Boag, William, et al.. (2016). MUTT: Metric Unit TesTing for Language Generation Tasks. 1935–1943. 2 indexed citations
12.
Boag, William, Peter Potash, & Anna Rumshisky. (2015). TwitterHawk: A Feature Bucket Based Approach to Sentiment Analysis. 640–646. 5 indexed citations

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.

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