Abigail Green‐Saxena

1.3k total citations
28 papers, 850 citations indexed

About

Abigail Green‐Saxena is a scholar working on Epidemiology, Artificial Intelligence and Molecular Biology. According to data from OpenAlex, Abigail Green‐Saxena has authored 28 papers receiving a total of 850 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Epidemiology, 7 papers in Artificial Intelligence and 5 papers in Molecular Biology. Recurrent topics in Abigail Green‐Saxena's work include Sepsis Diagnosis and Treatment (8 papers), Machine Learning in Healthcare (7 papers) and COVID-19 diagnosis using AI (5 papers). Abigail Green‐Saxena is often cited by papers focused on Sepsis Diagnosis and Treatment (8 papers), Machine Learning in Healthcare (7 papers) and COVID-19 diagnosis using AI (5 papers). Abigail Green‐Saxena collaborates with scholars based in United States, United Kingdom and Belgium. Abigail Green‐Saxena's co-authors include Ritankar Das, Jana Hoffman, Victoria J. Orphan, Emily Pellegrini, Jacob Calvert, Angier Allen, Sidney Le, Andrea J. McCoy, Qingqing Mao and Samson Mataraso and has published in prestigious journals such as Methods in enzymology on CD-ROM/Methods in enzymology, Critical Care Medicine and Frontiers in Microbiology.

In The Last Decade

Abigail Green‐Saxena

27 papers receiving 811 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Abigail Green‐Saxena United States	17	218	189	164	162	142	28	850
Zixin Hu China	20	183 0.8×	109 0.6×	199 1.2×	180 1.1×	22 0.2×	52	1.4k
Gang Yu China	15	90 0.4×	86 0.5×	39 0.2×	182 1.1×	20 0.1×	59	809
Amita Singh United States	21	150 0.7×	33 0.2×	598 3.6×	67 0.4×	24 0.2×	115	1.8k
Bing Xie China	11	155 0.7×	110 0.6×	52 0.3×	132 0.8×	8 0.1×	32	817
Jia He China	20	213 1.0×	37 0.2×	11 0.1×	189 1.2×	30 0.2×	91	1.0k
Trần Ngọc Đăng Vietnam	21	59 0.3×	44 0.2×	30 0.2×	62 0.4×	12 0.1×	78	1.2k
Kristian Hindberg Norway	16	108 0.5×	90 0.5×	33 0.2×	119 0.7×	15 0.1×	53	869
Jingyi Wu China	14	29 0.1×	28 0.1×	17 0.1×	175 1.1×	36 0.3×	50	649

Countries citing papers authored by Abigail Green‐Saxena

Since Specialization

Citations

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

Fields of papers citing papers by Abigail Green‐Saxena

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abigail Green‐Saxena

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

All Works

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20 of 20 papers shown

Allen, Angier, et al.. (2022). Prediction of diabetic kidney disease with machine learning algorithms, upon the initial diagnosis of type 2 diabetes mellitus. BMJ Open Diabetes Research & Care. 10(1). e002560–e002560. 50 indexed citations

Le, Sidney, Abigail Green‐Saxena, Jacob Calvert, et al.. (2022). Mortality, disease progression, and disease burden of acute kidney injury in alcohol use disorder subpopulation. The American Journal of the Medical Sciences. 364(1). 46–52. 2 indexed citations

Mataraso, Samson, Gina Barnes, Sepideh Shokouhi, et al.. (2022). Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm. Frontiers in Neurology. 12. 784250–784250. 3 indexed citations

Lam, Carson, Chak Foon Tso, Abigail Green‐Saxena, et al.. (2021). Semisupervised Deep Learning Techniques for Predicting Acute Respiratory Distress Syndrome From Time-Series Clinical Data: Model Development and Validation Study. JMIR Formative Research. 5(9). e28028–e28028. 13 indexed citations

Tso, Chak Foon, Anurag Garikipati, Abigail Green‐Saxena, Qingqing Mao, & Ritankar Das. (2021). Correlation of Population SARS-CoV-2 Cycle Threshold Values to Local Disease Dynamics: Exploratory Observational Study. JMIR Public Health and Surveillance. 7(6). e28265–e28265. 19 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

Le, Sidney, Angier Allen, Jacob Calvert, et al.. (2021). Convolutional Neural Network Model for Intensive Care Unit Acute Kidney Injury Prediction. Kidney International Reports. 6(5). 1289–1298. 40 indexed citations

Calvert, Jacob, Emily Pellegrini, Abigail Green‐Saxena, et al.. (2021). Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays. Clinical Imaging. 80. 268–273. 6 indexed citations

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

10.

Burdick, Hoyt, Andrea J. McCoy, Carol Gu, et al.. (2020). Effect of a sepsis prediction algorithm on patient mortality, length of stay and readmission: a prospective multicentre clinical outcomes evaluation of real-world patient data from US hospitals. BMJ Health & Care Informatics. 27(1). e100109–e100109. 48 indexed citations

11.

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

12.

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

13.

Burdick, Hoyt, Carol Gu, Jonathan Roberts, et al.. (2020). Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals. BMC Medical Informatics and Decision Making. 20(1). 276–276. 29 indexed citations

14.

Le, Sidney, Emily Pellegrini, Abigail Green‐Saxena, et al.. (2020). Supervised machine learning for the early prediction of acute respiratory distress syndrome (ARDS). Journal of Critical Care. 60. 96–102. 58 indexed citations

15.

Lam, Carson, Samson Mataraso, Angier Allen, et al.. (2020). Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study. Annals of Medicine and Surgery. 59. 207–216. 48 indexed citations

16.

Mohamadlou, Hamid, J.R. Calvert, Sidney Le, et al.. (2019). Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction. Health Informatics Journal. 26(3). 1912–1925. 15 indexed citations

17.

Green‐Saxena, Abigail, et al.. (2017). Neural crest and cancer: Divergent travelers on similar paths. Mechanisms of Development. 148. 89–99. 40 indexed citations

18.

Yang, Song, Michael Konopka, Abigail Green‐Saxena, et al.. (2013). Global Molecular Analyses of Methane Metabolism in Methanotrophic Alphaproteobacterium, Methylosinus trichosporium OB3b. Part II. Metabolomics and 13C-Labeling Study. Frontiers in Microbiology. 4. 70–70. 64 indexed citations

19.

Green‐Saxena, Abigail, Anne Dekas, Nathan F. Dalleska, & Victoria J. Orphan. (2013). Nitrate-based niche differentiation by distinct sulfate-reducing bacteria involved in the anaerobic oxidation of methane. The ISME Journal. 8(1). 150–163. 50 indexed citations

20.

Green‐Saxena, Abigail, A. A. Feyzullayev, Casey R. J. Hubert, et al.. (2012). Active sulfur cycling by diverse mesophilic and thermophilic microorganisms in terrestrial mud volcanoes of A zerbaijan. Environmental Microbiology. 14(12). 3271–3286. 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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