John Eberhardt

800 total citations
18 papers, 565 citations indexed

About

John Eberhardt is a scholar working on Oncology, Surgery and Epidemiology. According to data from OpenAlex, John Eberhardt has authored 18 papers receiving a total of 565 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Oncology, 5 papers in Surgery and 4 papers in Epidemiology. Recurrent topics in John Eberhardt's work include Colorectal Cancer Screening and Detection (4 papers), Colorectal Cancer Surgical Treatments (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). John Eberhardt is often cited by papers focused on Colorectal Cancer Screening and Detection (4 papers), Colorectal Cancer Surgical Treatments (4 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). John Eberhardt collaborates with scholars based in United States, Israel and Serbia. John Eberhardt's co-authors include Alexander Stojadinovic, Jonathan A. Forsberg, Anton J. Bilchik, Patrick J. Boland, Rikard Wedin, John H. Healey, Aviram Nissan, Scott R. Steele, George E. Peoples and Steven L. Chen and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Journal of Bone and Joint Surgery.

In The Last Decade

John Eberhardt

18 papers receiving 552 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
John Eberhardt United States 13 250 197 120 82 60 18 565
S. P. Somashekhar India 11 156 0.6× 149 0.8× 107 0.9× 118 1.4× 41 0.7× 98 670
Arif Jamshed Pakistan 17 269 1.1× 327 1.7× 126 1.1× 54 0.7× 41 0.7× 85 838
Dan Assaf Israel 9 159 0.6× 164 0.8× 74 0.6× 86 1.0× 35 0.6× 34 497
Joost Huiskens Netherlands 14 140 0.6× 187 0.9× 96 0.8× 124 1.5× 24 0.4× 31 588
Caroline Taylor United States 15 185 0.7× 146 0.7× 207 1.7× 81 1.0× 25 0.4× 42 706
Awais Ashfaq United States 12 246 1.0× 74 0.4× 121 1.0× 111 1.4× 68 1.1× 50 613
Marilyn J. Borst United States 9 191 0.8× 304 1.5× 290 2.4× 29 0.4× 43 0.7× 13 747
Edward P. Ambinder United States 17 78 0.3× 135 0.7× 74 0.6× 38 0.5× 79 1.3× 32 631
Aaron B. Cohen United States 14 188 0.8× 154 0.8× 121 1.0× 47 0.6× 21 0.3× 52 645
Samuel Seery China 17 174 0.7× 296 1.5× 218 1.8× 134 1.6× 21 0.3× 70 958

Countries citing papers authored by John Eberhardt

Since Specialization
Citations

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

Fields of papers citing papers by John Eberhardt

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of John Eberhardt

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

All Works

18 of 18 papers shown
1.
Steele, Scott R., Anton J. Bilchik, Eric K. Johnson, et al.. (2014). Time-dependent Estimates of Recurrence and Survival in Colon Cancer: Clinical Decision Support System Tool Development for Adjuvant Therapy and Oncological Outcome Assessment. The American Surgeon. 80(5). 441–453. 12 indexed citations
2.
Eberhardt, John, Anton J. Bilchik, & Alexander Stojadinovic. (2012). Clinical decision support systems: Potential with pitfalls. Journal of Surgical Oncology. 105(5). 502–510. 31 indexed citations
3.
Stojadinovic, Alexander, Anton J. Bilchik, David Smith, et al.. (2012). Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model. Annals of Surgical Oncology. 20(1). 161–174. 43 indexed citations
4.
Schaub, Nicholas P., Meghna Alimchandani, Martha Quezado, et al.. (2012). A Novel Nomogram for Peritoneal Mesothelioma Predicts Survival. Annals of Surgical Oncology. 20(2). 555–561. 38 indexed citations
5.
Steele, Scott R., Anton J. Bilchik, John Eberhardt, et al.. (2012). Using Machine-Learned Bayesian Belief Networks to Predict Perioperative Risk of Clostridium Difficile Infection Following Colon Surgery. SHILAP Revista de lepidopterología. 1(5). e6–e6. 9 indexed citations
6.
Stojadinovic, Alexander, Thomas A. Summers, John Eberhardt, et al.. (2011). Consensus Recommendations for Advancing Breast Cancer: Risk Identification and Screening in Ethnically Diverse Younger Women. Journal of Cancer. 2. 210–227. 9 indexed citations
7.
Forsberg, Jonathan A., John Eberhardt, Patrick J. Boland, Rikard Wedin, & John H. Healey. (2011). Estimating Survival in Patients with Operable Skeletal Metastases: An Application of a Bayesian Belief Network. PLoS ONE. 6(5). e19956–e19956. 145 indexed citations
8.
Stojadinovic, Alexander, John Eberhardt, Scott B. Shawen, et al.. (2011). Development of a Prognostic Naïve Bayesian Classifier for Successful Treatment of Nonunions. Journal of Bone and Joint Surgery. 93(2). 187–194. 31 indexed citations
9.
Stojadinovic, Alexander, Aviram Nissan, John Eberhardt, et al.. (2011). Development of a Bayesian Belief Network Model for Personalized Prognostic Risk Assessment in Colon Carcinomatosis. The American Surgeon. 77(2). 221–230. 27 indexed citations
10.
Elster, Eric A., Jason Hawksworth, David B. Leeser, et al.. (2010). Probabilistic (Bayesian) Modeling of Gene Expression in Transplant Glomerulopathy. Journal of Molecular Diagnostics. 12(5). 653–663. 10 indexed citations
11.
Smith, Heath A., Brett Maricque, John Eberhardt, et al.. (2010). IgG Responses to Tissue‐Associated Antigens as Biomarkers of Immunological Treatment Efficacy. BioMed Research International. 2011(1). 454861–454861. 12 indexed citations
12.
Chen, Steven L., Scott R. Steele, John Eberhardt, et al.. (2010). Lymph Node Ratio as a Quality and Prognostic Indicator in Stage III Colon Cancer. Annals of Surgery. 253(1). 82–87. 73 indexed citations
13.
Nissan, Aviram, Mladjan Protić, Anton J. Bilchik, et al.. (2010). Predictive Model of Outcome of Targeted Nodal Assessment in Colorectal Cancer. Annals of Surgery. 251(2). 265–274. 22 indexed citations
14.
Stojadinovic, Alexander, Christina Eberhardt, Leonard R. Henry, et al.. (2010). Development of a Bayesian classifier for breast cancer risk stratification: a feasibility study.. PubMed. 10. e25–e25. 13 indexed citations
15.
Stojadinovic, Alexander, George E. Peoples, Steven K. Libutti, et al.. (2009). Development of a clinical decision model for thyroid nodules. BMC Surgery. 9(1). 12–12. 41 indexed citations
16.
Ford, Eric W., et al.. (2009). Assessing Differences Between Physicians’ Realized and Anticipated Gains from Electronic Health Record Adoption. Journal of Medical Systems. 35(2). 151–161. 17 indexed citations
17.
Rudman, William J., et al.. (2009). Health Care Fraud and Abuse. PubMed. 6. 1g–1g. 31 indexed citations
18.
Eberhardt, John, et al.. (2009). ASSESSING DIFFERENCES BETWEEN REALIZED AND ANTICIPATED GAINS FROM ELECTRONIC HEALTH RECORD ADOPTION.. Academy of Management Proceedings. 2009(1). 1–6. 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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