Michele Bernardini

672 total citations
15 papers, 358 citations indexed

About

Michele Bernardini is a scholar working on Health Information Management, Artificial Intelligence and Public Health, Environmental and Occupational Health. According to data from OpenAlex, Michele Bernardini has authored 15 papers receiving a total of 358 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Health Information Management, 5 papers in Artificial Intelligence and 2 papers in Public Health, Environmental and Occupational Health. Recurrent topics in Michele Bernardini's work include Artificial Intelligence in Healthcare (9 papers), Machine Learning in Healthcare (5 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers). Michele Bernardini is often cited by papers focused on Artificial Intelligence in Healthcare (9 papers), Machine Learning in Healthcare (5 papers) and Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers). Michele Bernardini collaborates with scholars based in Italy, France and Belgium. Michele Bernardini's co-authors include Emanuele Frontoni, Luca Romeo, Daniele Liciotti, Paolo Misericordia, Micaela Morettini, Laura Burattini, Massih-Reza Amini, Paolo Di Bartolo, Antonio Nicolucci and Antonio Ceriello and has published in prestigious journals such as IEEE Access, Neurocomputing and Diabetes Research and Clinical Practice.

In The Last Decade

Michele Bernardini

14 papers receiving 349 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michele Bernardini Italy 8 162 135 99 40 39 15 358
Abid Sarwar India 11 184 1.1× 92 0.7× 67 0.7× 32 0.8× 82 2.1× 26 331
K. Shailaja India 7 98 0.6× 65 0.5× 33 0.3× 21 0.5× 38 1.0× 23 277
Deepta Rajan United States 8 213 1.3× 64 0.5× 33 0.3× 34 0.8× 59 1.5× 19 400
B. Seetharamulu India 8 121 0.7× 67 0.5× 26 0.3× 68 1.7× 37 0.9× 14 305
Bader Fahad Alkhamees Saudi Arabia 9 129 0.8× 76 0.6× 22 0.2× 19 0.5× 47 1.2× 20 274
Wojciech Książek Poland 8 208 1.3× 203 1.5× 40 0.4× 17 0.4× 120 3.1× 11 483
Vishu Madaan India 10 173 1.1× 31 0.2× 99 1.0× 15 0.4× 68 1.7× 38 338
Erik Dovgan Slovenia 11 73 0.5× 65 0.5× 53 0.5× 56 1.4× 25 0.6× 33 314
Tae Joon Jun South Korea 11 86 0.5× 33 0.2× 69 0.7× 10 0.3× 99 2.5× 46 397
Nikos Fazakis Greece 9 191 1.2× 91 0.7× 29 0.3× 19 0.5× 19 0.5× 25 318

Countries citing papers authored by Michele Bernardini

Since Specialization
Citations

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

Fields of papers citing papers by Michele Bernardini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michele Bernardini

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

All Works

15 of 15 papers shown
1.
Nicolucci, Antonio, Giacomo Vespasiani, Domenico Mannino, et al.. (2025). A machine learning algorithm for the prediction of complications incorporated in electronic medical records improves type 2 diabetes care. Diabetes Research and Clinical Practice. 229. 112900–112900.
2.
Bernardini, Michele, et al.. (2023). A novel missing data imputation approach based on clinical conditional Generative Adversarial Networks applied to EHR datasets. Computers in Biology and Medicine. 163. 107188–107188. 18 indexed citations
3.
Nicolucci, Antonio, Luca Romeo, Michele Bernardini, et al.. (2022). Prediction of complications of type 2 Diabetes: A Machine learning approach. Diabetes Research and Clinical Practice. 190. 110013–110013. 34 indexed citations
4.
Bernardini, Michele, Luca Romeo, Adriano Mancini, & Emanuele Frontoni. (2021). A Clinical Decision Support System to Stratify the Temporal Risk of Diabetic Retinopathy. IEEE Access. 9. 151864–151872. 6 indexed citations
5.
Bernardini, Michele, Luca Romeo, Emanuele Frontoni, & Massih-Reza Amini. (2021). A Semi-Supervised Multi-Task Learning Approach for Predicting Short-Term Kidney Disease Evolution. IEEE Journal of Biomedical and Health Informatics. 25(10). 3983–3994. 20 indexed citations
6.
Bernardini, Michele, Micaela Morettini, Luca Romeo, Emanuele Frontoni, & Laura Burattini. (2020). Early temporal prediction of Type 2 Diabetes Risk Condition from a General Practitioner Electronic Health Record: A Multiple Instance Boosting Approach. Artificial Intelligence in Medicine. 105. 101847–101847. 31 indexed citations
7.
Frontoni, Emanuele, Luca Romeo, Michele Bernardini, et al.. (2020). A Decision Support System for Diabetes Chronic Care Models Based on General Practitioner Engagement and EHR Data Sharing. IEEE Journal of Translational Engineering in Health and Medicine. 8. 1–12. 14 indexed citations
8.
Bernardini, Michele, Micaela Morettini, Luca Romeo, Emanuele Frontoni, & Laura Burattini. (2019). TyG-er: An ensemble Regression Forest approach for identification of clinical factors related to insulin resistance condition using Electronic Health Records. Computers in Biology and Medicine. 112. 103358–103358. 23 indexed citations
9.
Bernardini, Michele, Luca Romeo, Paolo Misericordia, & Emanuele Frontoni. (2019). Discovering the Type 2 Diabetes in Electronic Health Records Using the Sparse Balanced Support Vector Machine. IEEE Journal of Biomedical and Health Informatics. 24(1). 235–246. 85 indexed citations
10.
Ferri, Alessandro, Riccardo Rosati, Michele Bernardini, et al.. (2019). Towards the Design of a Machine Learning-based Consumer Healthcare Platform powered by Electronic Health Records and measurement of Lifestyle through Smartphone Data. IRIS eCampus Telematic University (Università degli Studi eCampus). 37–40. 2 indexed citations
11.
Liciotti, Daniele, Michele Bernardini, Luca Romeo, & Emanuele Frontoni. (2019). A sequential deep learning application for recognising human activities in smart homes. Neurocomputing. 396. 501–513. 117 indexed citations
12.
Bernardini, Michele, Alessandro Ferri, Lucia Migliorelli, et al.. (2019). Augmented Microscopy for DNA Damage Quantification: A Machine Learning Tool for Environmental, Medical and Health Sciences. IRIS eCampus Telematic University (Università degli Studi eCampus). 2 indexed citations
13.
Paolanti, Marina, et al.. (2018). Machine learning-based approaches to analyse and improve the diagnosis of endothelial dysfunction. IRIS eCampus Telematic University (Università degli Studi eCampus). 12. 1–6. 2 indexed citations
14.
Bernardini, Michele, et al.. (2017). A Clinical Decision Support System for Chronic Venous Insufficiency. IRIS eCampus Telematic University (Università degli Studi eCampus). 3 indexed citations
15.
Innocenti, Bernardo, Pierre Lambert, Silvia Pianigiani, et al.. (2016). Development of an automatic procedure to mechanically characterize soft tissue materials. IRIS eCampus Telematic University (Università degli Studi eCampus). 27. 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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