Erina Ghosh

656 total citations
27 papers, 400 citations indexed

About

Erina Ghosh is a scholar working on Cardiology and Cardiovascular Medicine, Nephrology and Epidemiology. According to data from OpenAlex, Erina Ghosh has authored 27 papers receiving a total of 400 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Cardiology and Cardiovascular Medicine, 8 papers in Nephrology and 6 papers in Epidemiology. Recurrent topics in Erina Ghosh's work include Cardiovascular Function and Risk Factors (11 papers), Acute Kidney Injury Research (8 papers) and Sepsis Diagnosis and Treatment (6 papers). Erina Ghosh is often cited by papers focused on Cardiovascular Function and Risk Factors (11 papers), Acute Kidney Injury Research (8 papers) and Sepsis Diagnosis and Treatment (6 papers). Erina Ghosh collaborates with scholars based in United States, United Kingdom and India. Erina Ghosh's co-authors include Sándor J. Kovács, Larry J. Eshelman, Kianoush Kashani, Simeng Zhu, Eric Seymour, Theodore J. Kolias, Robert D. Brook, Alan B. Weder, Scott L. Hummel and Nicolas W. Chbat and has published in prestigious journals such as Journal of the American College of Cardiology, Journal of Applied Physiology and Critical Care Medicine.

In The Last Decade

Erina Ghosh

25 papers receiving 391 citations

Peers

Erina Ghosh
Julian S. Haimovich United States
Vesna Degoricija Croatia
Shibba Takkar Chhabra India
Salvatore DiSomma United States
Hasanat Sharif Pakistan
Lorraine Kearney United Kingdom
Gorazd Voga Slovenia
Maurício de Nassau Machado Brazil
Sergio Cannizzaro Italy
Giorgio Maringhini Italy
Julian S. Haimovich United States
Erina Ghosh
Citations per year, relative to Erina Ghosh Erina Ghosh (= 1×) peers Julian S. Haimovich

Countries citing papers authored by Erina Ghosh

Since Specialization
Citations

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

Fields of papers citing papers by Erina Ghosh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Erina Ghosh

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

All Works

20 of 20 papers shown
1.
Khanna, Ashish K., Takahiro Kinoshita, Annamalai Natarajan, et al.. (2023). Association of systolic, diastolic, mean, and pulse pressure with morbidity and mortality in septic ICU patients: a nationwide observational study. Annals of Intensive Care. 13(1). 9–9. 20 indexed citations
2.
Feng, Ting, David P. Noren, Sara Mariani, et al.. (2023). Machine learning-based clinical decision support for infection risk prediction. Frontiers in Medicine. 10. 1213411–1213411. 4 indexed citations
3.
Schwager, Emma, Erina Ghosh, Larry J. Eshelman, et al.. (2023). Accurate and interpretable prediction of ICU-acquired AKI. Journal of Critical Care. 75. 154278–154278. 8 indexed citations
4.
Schwager, Emma, Erina Ghosh, Larry J. Eshelman, et al.. (2021). Including urinary output to define AKI enhances the performance of machine learning models to predict AKI at admission. Journal of Critical Care. 62. 283–288. 4 indexed citations
5.
Ghosh, Erina, Larry J. Eshelman, Emma Schwager, et al.. (2021). Estimation of Baseline Serum Creatinine with Machine Learning. American Journal of Nephrology. 52(9). 753–762. 5 indexed citations
6.
Swearingen, Dennis, Gregory Boverman, Kristen Tgavalekos, et al.. (2021). A Retrospective Cohort Study of Clinical Factors Associated with Transitions of Care among COVID-19 Patients. Journal of Clinical Medicine. 10(19). 4605–4605. 1 indexed citations
7.
8.
Shawwa, Khaled, et al.. (2020). Predicting acute kidney injury in critically ill patients using comorbid conditions utilizing machine learning. Clinical Kidney Journal. 14(5). 1428–1435. 25 indexed citations
9.
Chbat, Nicolas W., et al.. (2019). Automated Continuous Acute Kidney Injury Prediction and Surveillance: A Random Forest Model. Mayo Clinic Proceedings. 94(5). 783–792. 59 indexed citations
10.
Ghosh, Erina, et al.. (2018). Descriptive study of differences in acute kidney injury progression patterns in General and Cardiac Intensive Care Units. Journal of the Intensive Care Society. 20(3). 216–222. 2 indexed citations
11.
Ghosh, Erina, Larry J. Eshelman, Lin Yang, Eric J. Carlson, & Bill Lord. (2017). Description of vital signs data measurement frequency in a medical/surgical unit at a community hospital in United States. Data in Brief. 16. 612–616. 13 indexed citations
12.
Ghosh, Erina, Larry J. Eshelman, Lin Yang, Eric J. Carlson, & Bill Lord. (2017). Early Deterioration Indicator: Data-driven approach to detecting deterioration in general ward. Resuscitation. 122. 99–105. 27 indexed citations
13.
Bose, Somnath, et al.. (2017). 57: DIURNAL VARIATION IN SEDATIVE AND ANALGESIC USE IN ICUs: A RETROSPECTIVE COHORT STUDY. Critical Care Medicine. 46(1). 29–29.
14.
Zhu, Simeng, Astrid Apor, Béla Merkely, et al.. (2014). Diastolic function alteration mechanisms in physiologic hypertrophy versus pathologic hypertrophy are elucidated by model-based Doppler E-wave analysis. Journal of Exercise Science & Fitness. 12(2). 88–95. 3 indexed citations
15.
Mossahebi, Sina, Simeng Zhu, Howard Chen, et al.. (2014). Quantification of Global Diastolic Function by Kinematic Modeling-based Analysis of Transmitral Flow via the Parametrized Diastolic Filling Formalism. Journal of Visualized Experiments. e51471–e51471. 6 indexed citations
16.
Apor, Astrid, Béla Merkely, Simeng Zhu, et al.. (2013). Diastolic function in Olympic athletes versus controls: Stiffness-based and relaxation-based echocardiographic comparisons. Journal of Exercise Science & Fitness. 11(1). 29–34. 8 indexed citations
17.
Ghosh, Erina & Sándor J. Kovács. (2013). Early Left Ventricular Diastolic Function Quantitation Using Directional Impedances. Annals of Biomedical Engineering. 41(6). 1269–1278. 5 indexed citations
18.
Ghosh, Erina & Sándor J. Kovács. (2013). The quest for load-independent left ventricular chamber properties: Exploring the normalized pressure phase plane. Physiological Reports. 1(3). e00043–e00043. 6 indexed citations
19.
Ghosh, Erina & Sándor J. Kovács. (2012). LONGITUDINAL AND TRANSVERSE IMPEDANCE CAN QUANTIFY LEFT VENTRICULAR DIASTOLIC FUNCTION. Journal of the American College of Cardiology. 59(13). E1063–E1063. 1 indexed citations
20.
Ghosh, Erina & Sándor J. Kovács. (2011). E-WAVE ASSOCIATED VORTEX FORMATION FACILITATES DIASTATIC MITRAL LEAFLET COAPTATION. Journal of the American College of Cardiology. 57(14). E663–E663. 1 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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