Anna Siefkas

547 total citations
17 papers, 325 citations indexed

About

Anna Siefkas is a scholar working on Epidemiology, Infectious Diseases and Artificial Intelligence. According to data from OpenAlex, Anna Siefkas has authored 17 papers receiving a total of 325 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Epidemiology, 3 papers in Infectious Diseases and 3 papers in Artificial Intelligence. Recurrent topics in Anna Siefkas's work include SARS-CoV-2 and COVID-19 Research (3 papers), COVID-19 Clinical Research Studies (3 papers) and Sepsis Diagnosis and Treatment (3 papers). Anna Siefkas is often cited by papers focused on SARS-CoV-2 and COVID-19 Research (3 papers), COVID-19 Clinical Research Studies (3 papers) and Sepsis Diagnosis and Treatment (3 papers). Anna Siefkas collaborates with scholars based in United States, Belgium and Australia. Anna Siefkas's co-authors include Ritankar Das, Gina Barnes, Jana Hoffman, Jacob Calvert, Emily Pellegrini, Abigail Green‐Saxena, Qingqing Mao, Hoyt Burdick, Samson Mataraso and Gregory L. Braden and has published in prestigious journals such as Medicine, Epidemiologic Reviews and Applied Sciences.

In The Last Decade

Anna Siefkas

16 papers receiving 304 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Anna Siefkas United States	9	94	81	72	46	41	17	325
Gina Barnes United States	13	109 1.2×	129 1.6×	104 1.4×	78 1.7×	43 1.0×	31	486
Shorabuddin Syed United States	10	123 1.3×	90 1.1×	41 0.6×	52 1.1×	35 0.9×	28	527
Agni Orfanoudaki United States	7	70 0.7×	126 1.6×	73 1.0×	45 1.0×	58 1.4×	16	329
Chenxi Huang United States	11	66 0.7×	93 1.1×	100 1.4×	80 1.7×	48 1.2×	39	600
Nancy Gentry United States	3	48 0.5×	103 1.3×	67 0.9×	59 1.3×	13 0.3×	4	421
Siddharth Gosavi India	8	55 0.6×	40 0.5×	33 0.5×	73 1.6×	62 1.5×	24	297
Jordan Swartz United States	12	51 0.5×	34 0.4×	30 0.4×	19 0.4×	24 0.6×	25	359
Siavash Bolourani United States	9	78 0.8×	37 0.5×	57 0.8×	31 0.7×	31 0.8×	14	286
Renata R. Almeida United States	11	119 1.3×	22 0.3×	53 0.7×	56 1.2×	26 0.6×	22	348
Samson Mataraso United States	10	149 1.6×	219 2.7×	218 3.0×	109 2.4×	56 1.4×	22	559

Countries citing papers authored by Anna Siefkas

Since Specialization

Citations

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

Fields of papers citing papers by Anna Siefkas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Anna Siefkas

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

All Works

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17 of 17 papers shown

Javadi, Dena, Tamara Rushovich, Emily Wright, et al.. (2023). Systematic review of how racialized health inequities are addressed in Epidemiologic Reviews articles (1979–2021): a critical conceptual and empirical content analysis and recommendations for best practices. Epidemiologic Reviews. 45(1). 1–14. 1 indexed citations

Siefkas, Anna, Courtney Millar, Alyssa B. Dufour, et al.. (2022). Dairy Food Intake Is Not Associated With Frailty in Adults From the Framingham Heart Study. Journal of the Academy of Nutrition and Dietetics. 123(5). 729–739.e1. 8 indexed citations

Siefkas, Anna, et al.. (2022). Foot pain and inflammatory markers: a cross sectional study in older adults. Journal of Foot and Ankle Research. 15(1). 57–57. 3 indexed citations

Siefkas, Anna, et al.. (2022). Machine Learning Applications and Advancements in Alcohol Use Disorder: A Systematic Review. medRxiv. 4 indexed citations

Thapa, Rahul, Anurag Garikipati, Anna Siefkas, et al.. (2021). Early prediction of severe acute pancreatitis using machine learning. Pancreatology. 22(1). 43–50. 32 indexed citations

Lam, Charles, Anna Siefkas, Nicole S. Zelin, et al.. (2021). Using Machine Learning as a Precision Medicine Approach for Remdesivir and Corticosteroids as COVID-19 Pharmacotherapies. Clinical Therapeutics. 1 indexed citations

Garikipati, Anurag, Anna Siefkas, Gina Barnes, et al.. (2021). A machine learning approach to predicting risk of myelodysplastic syndrome. Leukemia Research. 109. 106639–106639. 15 indexed citations

Lam, Carson, Anna Siefkas, Nicole S. Zelin, et al.. (2021). Machine Learning as a Precision-Medicine Approach to Prescribing COVID-19 Pharmacotherapy with Remdesivir or Corticosteroids. Clinical Therapeutics. 43(5). 871–885. 11 indexed citations

Lam, Carson, Jacob Calvert, Anna Siefkas, et al.. (2021). Personalized stratification of hospitalization risk amidst COVID-19: A machine learning approach. Health Policy and Technology. 10(3). 100554–100554. 7 indexed citations

10.

Calvert, Jacob, Keyvan Rahmani, Gina Barnes, et al.. (2021). Predicting ventilator-associated pneumonia with machine learning. Medicine. 100(23). e26246–e26246. 26 indexed citations

11.

Calvert, Jacob, Gina Barnes, Anna Siefkas, et al.. (2021). Machine Learning Algorithms to Identify Patients at Risk of Ventilator-Associated Pneumonia. A2773–A2773.

12.

Mataraso, Samson, Anna Siefkas, Emily Pellegrini, et al.. (2021). A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients. Clinical and Applied Thrombosis/Hemostasis. 27. 2875389713–2875389713. 31 indexed citations

13.

Siefkas, Anna, Ellen P. McCarthy, Bruce Leff, Alyssa B. Dufour, & Marian T. Hannan. (2021). Social Isolation and Falls Risk: Lack of Social Contacts Decreases the Likelihood of Bathroom Modification Among Older Adults With Fear of Falling. Journal of Applied Gerontology. 41(5). 1293–1300. 6 indexed citations

14.

Allen, Angier, Anna Siefkas, Emily Pellegrini, et al.. (2021). A Digital Twins Machine Learning Model for Forecasting Disease Progression in Stroke Patients. Applied Sciences. 11(12). 5576–5576. 43 indexed citations

15.

Allen, Angier, Samson Mataraso, Anna Siefkas, et al.. (2020). A Racially Unbiased, Machine Learning Approach to Prediction of Mortality: Algorithm Development Study. JMIR Public Health and Surveillance. 6(4). e22400–e22400. 35 indexed citations

16.

Burdick, Hoyt, Carson Lam, Samson Mataraso, et al.. (2020). Prediction of respiratory decompensation in Covid-19 patients using machine learning: The READY trial. Computers in Biology and Medicine. 124. 103949–103949. 96 indexed citations

17.

Burdick, Hoyt, Carson Lam, Samson Mataraso, et al.. (2020). Is Machine Learning a Better Way to Identify COVID-19 Patients Who Might Benefit from Hydroxychloroquine Treatment?—The IDENTIFY Trial. Journal of Clinical Medicine. 9(12). 3834–3834. 6 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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