Abigail Flower

593 citations
13 papers · 466 · h-index 7

Impact in

Papers in

Abigail Flower

13 papers receiving 455 citations

Peers

Abigail Flower
Comparison fields: 5 of 72
  • Cognitive Neuroscience 202
  • Psychiatry and Mental health 63
  • Health Information Management 18
  • Endocrine and Autonomic Systems 21
  • Neurology 26
Replace Hongwei Wen with:
Hongwei Wen China
Dong Woo Kang South Korea
P Rajesh India
Jonathan Ailon Canada
Deniz Tunçel Türkiye
Pedro F. Viana Portugal
René A. Colorado United States
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Matthew Engelhard United States
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Abigail Flower relative to Hongwei Wen China Hongwei Wen's profile →
Citations per field
00.5×3.6×
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Citations per year

Countries citing papers authored by Abigail Flower

Since Specialization
Citations

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

Fields of papers citing papers by Abigail Flower

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Abigail Flower, 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 Abigail Flower Line = papers co-authored together Abigail Flower links everyone, so they are left out of the graph.

All Works

13 of 13 papers shown
#Work
1 2003270
2 201183
3 201924
4 201020
5
Prediction of mortality in an intensive care unit using logistic regression and a hidden Markov model
201220
6 201518
7 201811
8 20175
9 20165
10 20184
11 20193
12 20202
13 20171

About Abigail Flower

Abigail Flower is a scholar working on Artificial Intelligence, Epidemiology, Surgery, Cardiology and Cardiovascular Medicine and Health Information Management, having authored 13 papers that have together received 466 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (6 papers), Hemodynamic Monitoring and Therapy (3 papers), Sepsis Diagnosis and Treatment (3 papers), Artificial Intelligence in Healthcare (2 papers), Heart Rate Variability and Autonomic Control (2 papers), Cardiac electrophysiology and arrhythmias (1 paper), Patient Satisfaction in Healthcare (1 paper) and Healthcare Technology and Patient Monitoring (1 paper). The work is most often cited by research in Cognitive Neuroscience (202 citations), Psychiatry and Mental health (63 citations), Health Information Management (18 citations), Endocrine and Autonomic Systems (21 citations) and Neurology (26 citations). Abigail Flower has collaborated with scholars based in United States and Russia. Frequent co-authors include Stewart H. Mostofsky, James J. Pekar, Michael A. Kraut, Melissa C. Goldberg, Susan Courtney, Michael T. Abrams, Martha B. Denckla, J. Randall Moorman, J. B. Delos and Douglas E. Lake. Their work appears in journals such as Experimental Biology and Medicine, Pediatric Research, Physiological Measurement, American Journal of Perinatology and Cognitive Brain Research.

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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