Nadia Antonucci

6 papers receiving 453 citations

Nadia Antonucci's Hit Papers

Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment 2020 · 362 citations
3620+2+4Years since publication100200300

Peers

Nadia Antonucci
Comparison fields: 5 of 147
  • Health Informatics 28
  • Health Information Management 32
  • Nephrology 23
  • Radiology, Nuclear Medicine and Imaging 57
  • Oral Surgery 16
Replace Taku Harada with:
Taku Harada Japan
Muhammad Naeem Pakistan
Mayank Agrawal India
Hema Sekhar Reddy Rajula Italy
Alessandro Santaniello Italy
Jana Zvárová Czechia
Deepak Kaji United States
Rajiv K. Sethi United States
Ronit Brodie Israel
Young Mi Park South Korea
Nadia Antonucci relative to Taku Harada Japan Taku Harada's profile →
Citations per field
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Citations per year

Countries citing papers authored by Nadia Antonucci

Since Specialization
Citations

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

Fields of papers citing papers by Nadia Antonucci

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

6 of 6 papers shown
#Work
1
Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment
Hit paper breakdown →
2020362
2 201942
3 201624
4 202116
5 20219
6 20186

About Nadia Antonucci

Nadia Antonucci is a scholar working on Surgery, Molecular Biology, Pulmonary and Respiratory Medicine, Genetics and Nephrology, having authored 6 papers that have together received 459 indexed citations. Recurring topics across this work include Genetic and Kidney Cyst Diseases (2 papers), Contact Dermatitis and Allergies (1 paper), Mast cells and histamine (1 paper), Artificial Intelligence in Healthcare (1 paper), Dental Implant Techniques and Outcomes (1 paper), Systemic Sclerosis and Related Diseases (1 paper), Artificial Intelligence in Healthcare and Education (1 paper) and Extracellular vesicles in disease (1 paper). The work is most often cited by research in Health Informatics (28 citations), Health Information Management (32 citations), Nephrology (23 citations), Radiology, Nuclear Medicine and Imaging (57 citations) and Oral Surgery (16 citations). Nadia Antonucci has collaborated with scholars based in Italy, Germany and Canada. Frequent co-authors include Giuseppe Verlato, Hema Sekhar Reddy Rajula, Mirko Manchia, Vassilios Fanos, Antonio Lupo, Simona Granata, Gianluigi Zaza, Giovanni Gambaro, Andrea Petretto and Gian Marco Ghiggeri. Their work appears in journals such as Clinical Journal of the American Society of Nephrology, International Journal of Molecular Sciences, Pediatric Allergy and Immunology, Kidney International and Medicina.

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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