Lucia Sacchi

126 papers receiving 2.5k citations

Lucia Sacchi's Hit Papers

Machine Learning Methods to Predict Diabetes Complications 2017 · 259 citations
2590+3+6Years since publication50100150200250

Peers

Lucia Sacchi
Comparison fields: 5 of 157
  • Health Information Management 369
  • Health Informatics 54
  • Rheumatology 248
  • Signal Processing 178
  • Artificial Intelligence 492
Replace Hee Hwang with:
Hee Hwang South Korea
Shuang Wang China
Peggy Peissig United States
Kenney Ng United States
Guergana Savova United States
Guotong Xie China
R. Beuscart France
Tingting Zhu China
Jennifer A. Pacheco United States
Peter R. Rijnbeek Netherlands
Lucia Sacchi relative to Hee Hwang South Korea Hee Hwang's profile →
Citations per field
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Countries citing papers authored by Lucia Sacchi

Since Specialization
Citations

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

Fields of papers citing papers by Lucia Sacchi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 132 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Machine Learning Methods to Predict Diabetes Complications
Hit paper breakdown →
2017259
2 2018141
3 2007111
4 2018103
5 201083
6 201176
7 201269
8 200868
9 202060
10 201758
11 202055
12 201753
13 199651
14
Multivariate Time Series Classification with Temporal Abstractions
200942
15 201742
16 201742
17 200738
18 201737
19 200936
20 201136

About Lucia Sacchi

Lucia Sacchi is a scholar working on Molecular Biology, Artificial Intelligence, Information Systems, Endocrinology, Diabetes and Metabolism and Health Information Management, having authored 132 papers that have together received 2.5k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (18 papers), Biomedical Text Mining and Ontologies (14 papers), Diabetes Management and Research (12 papers), Bioinformatics and Genomic Networks (11 papers), Time Series Analysis and Forecasting (10 papers), Machine Learning in Healthcare (8 papers), Artificial Intelligence in Healthcare (8 papers) and Semantic Web and Ontologies (8 papers). The work is most often cited by research in Health Information Management (369 citations), Health Informatics (54 citations), Rheumatology (248 citations), Signal Processing (178 citations) and Artificial Intelligence (492 citations). Lucia Sacchi has collaborated with scholars based in Italy, United States and United Kingdom. Frequent co-authors include Riccardo Bellazzi, Arianna Dagliati, Silvana Quaglini, Simone Marini, Valentina Tibollo, Luca Chiovato, Cristiana Larizza, Pasquale De Cata, Giulia Cogni and Enea Parimbelli. Their work appears in journals such as Artificial Intelligence in Medicine, Journal of Biomedical Informatics, Journal of Diabetes Science and Technology, International Journal of Medical Informatics and The International Journal of Developmental Biology.

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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