Daniel Fort

2.8k citations
46 papers · 1.6k · 1 hit paper · h-index 13

Impact in

Papers in

Daniel Fort

42 papers receiving 1.6k citations

Daniel Fort's Hit Papers

Hospitalization and Mortality among Black Patients and White Patients with Covid-19 2020 · 1.1k citations
1.1k0+2+4Years since publication2505007501000

Peers

Daniel Fort
Comparison fields: 5 of 130
  • Modeling and Simulation 187
  • Infectious Diseases 619
  • Health 172
  • Oncology 468
  • Neurology 226
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Daniel Fort relative to Leonardo Seoane United States Leonardo Seoane's profile →
Citations per field
00.5×1.5×
Leonardo Seoane · 1×
Citations per year

Countries citing papers authored by Daniel Fort

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Fort

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Hospitalization and Mortality among Black Patients and White Patients with Covid-19
Hit paper breakdown →
20201140
2 2017104
3 202350
4 201745
5 202026
6 202123
7 202120
8 202019
9 202219
10 202316
11 202215
12
Considerations for using research data to verify clinical data accuracy.
201413
13 202013
14 202110
15 20228
16 20227
17 20217
18 20216
19 20206
20
Classifying Clinical Trial Eligibility Criteria to Facilitate Phased Cohort Identification Using Clinical Data Repositories.
20176

About Daniel Fort

Daniel Fort is a scholar working on Epidemiology, Pulmonary and Respiratory Medicine, Hepatology, Cardiology and Cardiovascular Medicine and Infectious Diseases, having authored 46 papers that have together received 1.6k indexed citations. Recurring topics across this work include Hepatocellular Carcinoma Treatment and Prognosis (8 papers), Cerebrovascular and Carotid Artery Diseases (8 papers), Acute Ischemic Stroke Management (8 papers), Cardiovascular Health and Disease Prevention (4 papers), Liver Disease Diagnosis and Treatment (4 papers), SARS-CoV-2 and COVID-19 Research (4 papers), COVID-19 epidemiological studies (4 papers) and Chronic Disease Management Strategies (4 papers). The work is most often cited by research in Modeling and Simulation (187 citations), Infectious Diseases (619 citations), Health (172 citations), Oncology (468 citations) and Neurology (226 citations). Daniel Fort has collaborated with scholars based in United States, Australia and Vietnam. Frequent co-authors include Eboni G. Price‐Haywood, Leonardo Seoane, Jeffrey H. Burton, Pamela L Shaw, Justin Starren, Timothy M. Herr, Julia Garcia‐Diaz, Amy K. Feehan, Faraz S. Ahmad and Abel Kho. Their work appears in journals such as Journal of Vascular Surgery, Cancers, Emerging infectious diseases, Medical Care and Frontiers in Immunology.

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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