Amber Stubbs

1.7k total citations
19 papers, 978 citations indexed

About

Amber Stubbs is a scholar working on Artificial Intelligence, Molecular Biology and Health Information Management. According to data from OpenAlex, Amber Stubbs has authored 19 papers receiving a total of 978 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 10 papers in Molecular Biology and 3 papers in Health Information Management. Recurrent topics in Amber Stubbs's work include Biomedical Text Mining and Ontologies (10 papers), Topic Modeling (7 papers) and Machine Learning in Healthcare (5 papers). Amber Stubbs is often cited by papers focused on Biomedical Text Mining and Ontologies (10 papers), Topic Modeling (7 papers) and Machine Learning in Healthcare (5 papers). Amber Stubbs collaborates with scholars based in United States. Amber Stubbs's co-authors include Özlem Uzuner, Michele Filannino, James Pustejovsky, Christopher Kotfila, Sam Henry, Hua Xu, Ergin Soysal, Vishesh Kumar, Stanley Y. Shaw and V. G. Vinod Vydiswaran and has published in prestigious journals such as Journal of the American Medical Informatics Association, Journal of Biomedical Informatics and Meeting of the Association for Computational Linguistics.

In The Last Decade

Amber Stubbs

18 papers receiving 932 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amber Stubbs United States 13 824 471 163 82 64 19 978
Louise Deléger France 18 521 0.6× 429 0.9× 113 0.7× 46 0.6× 35 0.5× 46 844
Hong-Jie Dai Taiwan 15 473 0.6× 328 0.7× 92 0.6× 26 0.3× 27 0.4× 65 755
François-Michel Lang United States 6 917 1.1× 929 2.0× 99 0.6× 45 0.5× 14 0.2× 7 1.2k
William Boag United States 6 715 0.9× 301 0.6× 93 0.6× 33 0.4× 99 1.5× 12 873
Meliha Yetisgen-Yildiz United States 15 498 0.6× 468 1.0× 58 0.4× 38 0.5× 36 0.6× 29 765
Aurélie Névéol France 19 944 1.1× 809 1.7× 79 0.5× 74 0.9× 75 1.2× 71 1.4k
Asma Ben Abacha United States 20 1.0k 1.3× 418 0.9× 55 0.3× 49 0.6× 98 1.5× 51 1.3k
Henk Harkema United States 12 401 0.5× 339 0.7× 76 0.5× 26 0.3× 16 0.3× 22 646
Mathias Brochhausen United States 16 336 0.4× 462 1.0× 107 0.7× 50 0.6× 16 0.3× 76 710
Nansu Zong United States 14 201 0.2× 322 0.7× 98 0.6× 57 0.7× 25 0.4× 46 644

Countries citing papers authored by Amber Stubbs

Since Specialization
Citations

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

Fields of papers citing papers by Amber Stubbs

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Amber Stubbs

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

All Works

19 of 19 papers shown
1.
Stubbs, Amber, Michele Filannino, Ergin Soysal, Sam Henry, & Özlem Uzuner. (2019). Cohort selection for clinical trials: n2c2 2018 shared task track 1. Journal of the American Medical Informatics Association. 26(11). 1163–1171. 46 indexed citations
2.
Henry, Sam, et al.. (2019). 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 27(1). 3–12. 178 indexed citations
3.
O’Hara, Keith J., et al.. (2018). Team-Teaching with Colleagues in the Arts and Humanities. 265–266. 1 indexed citations
4.
Stubbs, Amber, Michele Filannino, & Özlem Uzuner. (2017). De-identification of psychiatric intake records: Overview of 2016 CEGS N-GRID shared tasks Track 1. Journal of Biomedical Informatics. 75. S4–S18. 71 indexed citations
5.
Filannino, Michele, Amber Stubbs, & Özlem Uzuner. (2017). Symptom severity prediction from neuropsychiatric clinical records: Overview of 2016 CEGS N-GRID shared tasks Track 2. Journal of Biomedical Informatics. 75. S62–S70. 33 indexed citations
6.
Zheng, Kai, V. G. Vinod Vydiswaran, Yue Wang, et al.. (2015). Ease of adoption of clinical natural language processing software: An evaluation of five systems. Journal of Biomedical Informatics. 58. S189–S196. 25 indexed citations
7.
Stubbs, Amber, Christopher Kotfila, & Özlem Uzuner. (2015). Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/UTHealth shared task Track 1. Journal of Biomedical Informatics. 58. S11–S19. 148 indexed citations
8.
Stubbs, Amber, Christopher Kotfila, Hua Xu, & Özlem Uzuner. (2015). Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal of Biomedical Informatics. 58. S67–S77. 86 indexed citations
9.
Kumar, Vishesh, Amber Stubbs, Stanley Y. Shaw, & Özlem Uzuner. (2015). Creation of a new longitudinal corpus of clinical narratives. Journal of Biomedical Informatics. 58. S6–S10. 29 indexed citations
10.
Stubbs, Amber & Özlem Uzuner. (2015). Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus. Journal of Biomedical Informatics. 58. S20–S29. 135 indexed citations
11.
Stubbs, Amber & Özlem Uzuner. (2015). Annotating risk factors for heart disease in clinical narratives for diabetic patients. Journal of Biomedical Informatics. 58. S78–S91. 52 indexed citations
12.
Uzuner, Özlem, Meliha Yetişgen, & Amber Stubbs. (2014). Biomedical/Clinical NLP. International Conference on Computational Linguistics. 1–2. 2 indexed citations
13.
Pustejovsky, James & Amber Stubbs. (2013). A methodology for using professional knowledge in corpus annotation. 7 indexed citations
14.
Pustejovsky, James & Amber Stubbs. (2012). Natural Language Annotation for Machine Learning. CERN Document Server (European Organization for Nuclear Research). 92 indexed citations
15.
Stubbs, Amber. (2011). MAE and MAI: Lightweight Annotation and Adjudication Tools. 129–133. 20 indexed citations
16.
Pustejovsky, James & Amber Stubbs. (2011). Increasing Informativeness in Temporal Annotation. 152–160. 45 indexed citations
17.
Stubbs, Amber & Benjamin J. Harshfield. (2010). Applying the TARSQI Toolkit to Augment Text Mining of EHRs. Meeting of the Association for Computational Linguistics. 141–143. 3 indexed citations
18.
Verhagen, Marc, Amber Stubbs, & James Pustejovsky. (2007). Combining independent syntactic and semantic annotation schemes. 109–112. 5 indexed citations
19.
Gevelber, Michael, et al.. (2005). CVD dynamics for real-time control: multi-component and flow modelling. 2. 1220–1224.

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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