Rachel Ginn

1.5k total citations · 2 hit papers
8 papers, 1.0k citations indexed

About

Rachel Ginn is a scholar working on Molecular Biology, Toxicology and Epidemiology. According to data from OpenAlex, Rachel Ginn has authored 8 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Molecular Biology, 3 papers in Toxicology and 3 papers in Epidemiology. Recurrent topics in Rachel Ginn's work include Biomedical Text Mining and Ontologies (3 papers), Pharmacovigilance and Adverse Drug Reactions (3 papers) and Antifungal resistance and susceptibility (2 papers). Rachel Ginn is often cited by papers focused on Biomedical Text Mining and Ontologies (3 papers), Pharmacovigilance and Adverse Drug Reactions (3 papers) and Antifungal resistance and susceptibility (2 papers). Rachel Ginn collaborates with scholars based in United States and United Kingdom. Rachel Ginn's co-authors include Graciela Gonzalez‐Hernandez, Karen O’Connor, Abeed Sarker, Azadeh Nikfarjam, Karen L. Smith, Matthew Scotch, Dan Malone, Guillermo Méndez and John N. Galgiani and has published in prestigious journals such as Emerging infectious diseases, Journal of the American Medical Informatics Association and Drug Safety.

In The Last Decade

Rachel Ginn

8 papers receiving 948 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.

Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features

2015 376 citations Azadeh Nikfarjam, Abeed Sarker et al. Journal of the American Medical Informatics Association profile →
Utilizing social media data for pharmacovigilance: A review

2015 346 citations Abeed Sarker, Rachel Ginn et al. Journal of Biomedical Informatics profile →

Peers

Rachel Ginn

TOXIC

AI

MB

CTM

EPIDE

Azadeh Nikfarjam United States

Karen O’Connor United States

Eiji Aramaki Japan

Adrian Benton United States

Luca Toldo Germany

Abeed Sarker United States

Taha Kass‐Hout United States

Son Doan United States

Angus Roberts United Kingdom

C Friedman United States

Azadeh Nikfarjam United States

Rachel Ginn

505 ×1.3

TOXIC

554 ×1.6

AI

421 ×1.4

MB

287 ×1.5

CTM

130 ×0.9

EPIDE

Citations per year, relative to Rachel Ginn Rachel Ginn (= 1×) peers Azadeh Nikfarjam

Countries citing papers authored by Rachel Ginn

Since Specialization

Citations

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

Fields of papers citing papers by Rachel Ginn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rachel Ginn

This figure shows the co-authorship network connecting the top 25 collaborators of Rachel Ginn. A scholar is included among the top collaborators of Rachel Ginn 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 Rachel Ginn. Rachel Ginn 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:

8 of 8 papers shown

1.

Ginn, Rachel, et al.. (2019). Delays in Coccidioidomycosis Diagnosis and Relationship to Healthcare Utilization, Arizona, USA¹. Emerging infectious diseases. 25(9). 1742–1744. 20 indexed citations

2.

Ginn, Rachel, et al.. (2019). Delays in Coccidioidomycosis Diagnosis and Relationship to Healthcare Utilization, Phoenix, Arizona, USA¹. Emerging infectious diseases. 25(8). 1742–1744. 17 indexed citations

3.

Sarker, Abeed, Karen O’Connor, Rachel Ginn, et al.. (2016). Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter. Drug Safety. 39(3). 231–240. 136 indexed citations

4.

Sarker, Abeed, Rachel Ginn, Azadeh Nikfarjam, et al.. (2015). Utilizing social media data for pharmacovigilance: A review. Journal of Biomedical Informatics. 54. 202–212. 346 indexed citations breakdown →

5.

Nikfarjam, Azadeh, Abeed Sarker, Karen O’Connor, Rachel Ginn, & Graciela Gonzalez‐Hernandez. (2015). Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features. Journal of the American Medical Informatics Association. 22(3). 671–681. 376 indexed citations breakdown →

6.

Nikfarjam, Azadeh, et al.. (2014). Unsupervised gene function extraction using semantic vectors. Database. 2014(0). bau084–bau084. 3 indexed citations

7.

O’Connor, Karen, et al.. (2014). Pharmacovigilance on twitter? Mining tweets for adverse drug reactions.. PubMed. 2014. 924–33. 87 indexed citations

8.

Ginn, Rachel, et al.. (2001). Challenging the myth of the 12-hour shift: a pilot evaluation. Nursing Standard. 15(29). 33–36. 23 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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