Brandon Arnieri
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Statistics and Probability top 5%
- Statistical Methods in Clinical Trials
- Advanced Causal Inference Techniques
Papers in
-
- Lung Cancer Treatments and Mutations 3
- Lung Cancer Diagnosis and Treatment 2
-
- Cancer Genomics and Diagnostics 3
- Co-authors
- William B. Capra (6 shared papers)Gillis Carrigan (5 shared papers)Michael D. Taylor (4 shared papers)Samuel Whipple (3 shared papers)Ryan Copping (2 shared papers)Michael W. Lu (2 shared papers)Melisa Tucker (4 shared papers)Sandra D. Griffith (2 shared papers)
- Journals
- Journal of Clinical Oncology (2 papers)Pharmacoepidemiology and Drug Safety (1 paper)BMC Medical Research Methodology (1 paper)European Journal of Cancer (1 paper)Nature (1 paper)
- Partner nations
- United StatesSwitzerlandFrance
In The Last Decade
Brandon Arnieri
10 papers receiving 510 citations
Brandon Arnieri's Hit Papers
Peers
Comparison fields: 5 of 62
- Health Informatics 47
- Statistics and Probability 97
- Cancer Research 59
- Oncology 91
- Pulmonary and Respiratory Medicine 85
Countries citing papers authored by Brandon Arnieri
This map shows the geographic impact of Brandon Arnieri'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 Brandon Arnieri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brandon Arnieri more than expected).
Fields of papers citing papers by Brandon Arnieri
This network shows the impact of papers produced by Brandon Arnieri. 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 Brandon Arnieri. The network helps show where Brandon Arnieri may publish in the future.
Co-authors
The 25 scholars most cited alongside Brandon Arnieri, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Evaluating eligibility criteria of oncology trials using real-world data and AI Hit paper breakdown → | 2021 | 201 |
| 2 | 2018 | 168 | |
| 3 | 2019 | 73 | |
| 4 | 2019 | 43 | |
| 5 | 2019 | 23 | |
| 6 | 2014 | 5 | |
| 7 | 2016 | 3 | |
| 8 | 2017 | 2 | |
| 9 | 2017 | 1 | |
| 10 | 2018 | 1 |
About Brandon Arnieri
Brandon Arnieri is a scholar working on Pulmonary and Respiratory Medicine, Cancer Research, Oncology, Statistics and Probability and Molecular Biology, having authored 10 papers that have together received 520 indexed citations. Recurring topics across this work include Cancer Genomics and Diagnostics (3 papers), Lung Cancer Treatments and Mutations (3 papers), Statistical Methods in Clinical Trials (2 papers), Lung Cancer Diagnosis and Treatment (2 papers), Colorectal Cancer Treatments and Studies (2 papers), Radiomics and Machine Learning in Medical Imaging (1 paper), Chronic Lymphocytic Leukemia Research (1 paper) and Ethics in Clinical Research (1 paper). The work is most often cited by research in Health Informatics (47 citations), Statistics and Probability (97 citations), Cancer Research (59 citations), Oncology (91 citations) and Pulmonary and Respiratory Medicine (85 citations). Brandon Arnieri has collaborated with scholars based in United States, Switzerland and France. Frequent co-authors include William B. Capra, Gillis Carrigan, Michael D. Taylor, Samuel Whipple, Ryan Copping, Michael W. Lu, Melisa Tucker, Sandra D. Griffith, Aracelis Z. Torres and James Zou. Their work appears in journals such as Journal of Clinical Oncology, Pharmacoepidemiology and Drug Safety, BMC Medical Research Methodology, European Journal of Cancer and Nature.
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.