Roy J. Adams

1.7k total citations · 1 hit paper
104 papers, 1000 citations indexed

About

Roy J. Adams is a scholar working on Public Administration, Political Science and International Relations and Artificial Intelligence. According to data from OpenAlex, Roy J. Adams has authored 104 papers receiving a total of 1000 indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Public Administration, 16 papers in Political Science and International Relations and 9 papers in Artificial Intelligence. Recurrent topics in Roy J. Adams's work include Labor Movements and Unions (42 papers), International Labor and Employment Law (9 papers) and Machine Learning in Healthcare (6 papers). Roy J. Adams is often cited by papers focused on Labor Movements and Unions (42 papers), International Labor and Employment Law (9 papers) and Machine Learning in Healthcare (6 papers). Roy J. Adams collaborates with scholars based in United States, Canada and United Kingdom. Roy J. Adams's co-authors include Suchi Saria, Benjamin M. Marlin, Rajani S. Sadasivam, Thomas K. Houston, Clyde W. Summers, Hossein Soleimani, Mustapha Saheed, Katharine E. Henry, Albert W. Wu and Sara E. Cosgrove and has published in prestigious journals such as Nature Medicine, SHILAP Revista de lepidopterología and Academy of Management Review.

In The Last Decade

Roy J. Adams

92 papers receiving 788 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Prospective, multi-site study of patient outcomes after implementation of the TREWS machine learning-based early warning system for sepsis

2022 156 citations Roy J. Adams, Katharine E. Henry et al. Nature Medicine profile →

Peers

Roy J. Adams

PA

PSIR

GHP

AI

SPS

Agneta Ranerup Sweden

Giuseppe Scaratti Italy

Sue Jones United Kingdom

Lise Lamothe Canada

Naomi Aoki Singapore

Holly Jarman United States

Michael A. Santoro United States

Christine Urquhart United Kingdom

Christine B. Williams United States

Mara Gorli Italy

Agneta Ranerup Sweden

Roy J. Adams

59 ×0.2

PA

129 ×0.8

PSIR

185 ×1.2

GHP

26 ×0.2

AI

119 ×1.0

SPS

Citations per year, relative to Roy J. Adams Roy J. Adams (= 1×) peers Agneta Ranerup

Countries citing papers authored by Roy J. Adams

Since Specialization

Citations

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

Fields of papers citing papers by Roy J. Adams

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Roy J. Adams. 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 Roy J. Adams. The network helps show where Roy J. Adams may publish in the future.

Co-authorship network of co-authors of Roy J. Adams

This figure shows the co-authorship network connecting the top 25 collaborators of Roy J. Adams. A scholar is included among the top collaborators of Roy J. Adams 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 Roy J. Adams. Roy J. Adams is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Exarchos, Themis P., Konstantinos Lazaros, Aristidis G. Vrahatis, et al.. (2025). Five-year dementia prediction and decision support system based on real-world data. Frontiers in Aging Neuroscience. 17. 1670609–1670609.

2.

Adams, Roy J., Jeannie‐Marie Leoutsakos, Milap A. Nowrangi, et al.. (2025). Clinical factors predicting the rate of cognitive decline in a US memory clinic: An electronic health record study. Alzheimer s & Dementia Translational Research & Clinical Interventions. 11(2). e70070–e70070.

3.

Subbaswamy, Adarsh, et al.. (2024). A data-driven framework for identifying patient subgroups on which an AI/machine learning model may underperform. npj Digital Medicine. 7(1). 334–334. 6 indexed citations

4.

Haroz, Emily E., et al.. (2024). Performance of Machine Learning Suicide Risk Models in an American Indian Population. JAMA Network Open. 7(10). e2439269–e2439269. 6 indexed citations

5.

Adams, Roy J., Esther S. Oh, Sevil Yaşar, Constantine G. Lyketsos, & Jennifer S. Mammen. (2023). Endogenous and Exogenous Thyrotoxicosis and Risk of Incident Cognitive Disorders in Older Adults. JAMA Internal Medicine. 183(12). 1324–1324. 13 indexed citations

6.

Nowrangi, Milap A., Xin Li, Xin Xu, et al.. (2023). Translating clinical notes into quantitative measures—a real-world observation on the response to cholinesterase inhibitors or selective serotonin reuptake inhibitors prescribed to outpatients with dementia using electronic medical records. Frontiers in Pharmacology. 14. 1177026–1177026. 2 indexed citations

7.

Henry, Katharine E., Roy J. Adams, Hossein Soleimani, et al.. (2022). Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nature Medicine. 28(7). 1447–1454. 62 indexed citations

8.

Subbaswamy, Adarsh, Roy J. Adams, & Suchi Saria. (2021). Evaluating Model Robustness and Stability to Dataset Shift. International Conference on Artificial Intelligence and Statistics. 2611–2619. 2 indexed citations

9.

Adams, Roy J. & Benjamin M. Marlin. (2018). Learning Time Series Segmentation Models from Temporally Imprecise Labels. Uncertainty in Artificial Intelligence. 135–144. 1 indexed citations

10.

Adams, Roy J., et al.. (2016). Parsing wireless electrocardiogram signals with context free grammar conditional random fields. 149–156. 2 indexed citations

11.

Sadasivam, Rajani S., et al.. (2016). Impact of a Collective Intelligence Tailored Messaging System on Smoking Cessation: The Perspect Randomized Experiment. Journal of Medical Internet Research. 18(11). e285–e285. 44 indexed citations

12.

Adams, Roy J.. (2010). Efca, Alas, Is Not the Answer. Labor Studies in Working-Class History of the Americas. 7(3). 9–15.

13.

Adams, Roy J.. (2005). Efficiency, Equity, and Voice as Moral Imperatives. Employee Responsibilities and Rights Journal. 17(2). 111–117. 5 indexed citations

14.

Adams, Roy J.. (2002). The Wagner Act Model: A Toxic System beyond Repair. British Journal of Industrial Relations. 122–127. 1 indexed citations

15.

Adams, Roy J.. (1997). Minstrel – Management information software tool for European libraries. Program electronic library and information systems. 31(1). 33–45. 1 indexed citations

16.

Adams, Roy J.. (1996). Comparing Unions Cross-nationally: A Research Agenda. International journal of employment studies. 4(1). 127. 4 indexed citations

17.

Adams, Roy J.. (1990). Communication and delivery systems for librarians. 1 indexed citations

18.

Adams, Roy J.. (1986). Information technology & libraries : a future for academic libraries.

19.

Adams, Roy J.. (1985). Should Works Councils Be Used as Industrial Relations Policy. Monthly labor review. 108(7). 25. 8 indexed citations

20.

Adams, Roy J., et al.. (1985). Industrial relations in a decade of economic change. 30 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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