Jenna Reps

59 papers receiving 1.6k citations

Jenna Reps's Hit Papers

Can machine-learning improve cardiovascular risk prediction using routine clinical data? 2017 · 820 citations
8200+3+6Years since publication250500750

Peers

Jenna Reps
Comparison fields: 5 of 137
  • Health Informatics 187
  • Health Information Management 478
  • Medical Laboratory Technology 29
  • Toxicology 63
  • Artificial Intelligence 547
Replace Hsuan‐Chia Yang with:
Hsuan‐Chia Yang Taiwan
Md. Mohaimenul Islam Taiwan
Peter R. Rijnbeek Netherlands
Tahmina Nasrin Poly Taiwan
Wei‐Qi Wei United States
Chayakrit Krittanawong United States
Nicole G. Weiskopf United States
Shyam Visweswaran United States
Emmanuel Chazard France
Luke V. Rasmussen United States
Jenna Reps relative to Hsuan‐Chia Yang Taiwan Hsuan‐Chia Yang's profile →
Citations per field
00.5×1.5×2.1×
Hsuan‐Chia Yang · 1×
Citations per year

Countries citing papers authored by Jenna Reps

Since Specialization
Citations

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

Fields of papers citing papers by Jenna Reps

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Jenna Reps, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jenna Reps Line = papers co-authored together Jenna Reps links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 61 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Can machine-learning improve cardiovascular risk prediction using routine clinical data?
Hit paper breakdown →
2017820
2 2018119
3 201862
4 202047
5 201442
6 201939
7 201736
8 202430
9 202029
10 202125
11 201623
12 202122
13 202222
14 202022
15 201516
16 202115
17 201815
18 201915
19 201315
20 202014

About Jenna Reps

Jenna Reps is a scholar working on Artificial Intelligence, Epidemiology, Toxicology, Health Information Management and Public Health, Environmental and Occupational Health, having authored 61 papers that have together received 1.7k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (19 papers), Pharmacovigilance and Adverse Drug Reactions (8 papers), Chronic Disease Management Strategies (7 papers), Artificial Intelligence in Healthcare (6 papers), Health Systems, Economic Evaluations, Quality of Life (5 papers), Colorectal Cancer Screening and Detection (5 papers), Treatment of Major Depression (5 papers) and Computational Drug Discovery Methods (4 papers). The work is most often cited by research in Health Informatics (187 citations), Health Information Management (478 citations), Medical Laboratory Technology (29 citations), Toxicology (63 citations) and Artificial Intelligence (547 citations). Jenna Reps has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Jonathan M. Garibaldi, Stephen Weng, Nadeem Qureshi, Joe Kai, Patrick Ryan, Peter R. Rijnbeek, M. Soledad Cepeda, Martijn J. Schuemie, Marc A. Suchard and Uwe Aickelin. Their work appears in journals such as PLoS ONE, Drug Safety, BMC Medical Research Methodology, BMC Medical Informatics and Decision Making and Journal of the American Medical Informatics Association.

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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