Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort
Research of Inflammatory Bowel Disease (IBD) involves integrating diverse and heterogeneous data sources, from clinical records to imaging and laboratory results, which presents significant challenges in data harmonization and exploration. These challenges are also reflected in the development of ma...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Data |
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| Online Access: | https://www.mdpi.com/2306-5729/10/7/100 |
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| author | Aldo Marzullo Victor Savevski Maddalena Menini Alessandro Schilirò Gianluca Franchellucci Arianna Dal Buono Cristina Bezzio Roberto Gabbiadini Cesare Hassan Alessandro Repici Alessandro Armuzzi |
| author_facet | Aldo Marzullo Victor Savevski Maddalena Menini Alessandro Schilirò Gianluca Franchellucci Arianna Dal Buono Cristina Bezzio Roberto Gabbiadini Cesare Hassan Alessandro Repici Alessandro Armuzzi |
| author_sort | Aldo Marzullo |
| collection | DOAJ |
| description | Research of Inflammatory Bowel Disease (IBD) involves integrating diverse and heterogeneous data sources, from clinical records to imaging and laboratory results, which presents significant challenges in data harmonization and exploration. These challenges are also reflected in the development of machine-learning applications, where inconsistencies in data quality, missing information, and variability in data formats can adversely affect the performance and generalizability of models. In this study, we describe the collection and curation of a comprehensive dataset focused on IBD. In addition, we present a dedicated research platform. We focus on ethical standards, data protection, and seamless integration of different data types. We also discuss the challenges encountered, as well as the insights gained during its implementation. |
| format | Article |
| id | doaj-art-6fd65bede840407b8e9f4e7f6076902a |
| institution | Kabale University |
| issn | 2306-5729 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Data |
| spelling | doaj-art-6fd65bede840407b8e9f4e7f6076902a2025-08-20T03:58:31ZengMDPI AGData2306-57292025-06-0110710010.3390/data10070100Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian CohortAldo Marzullo0Victor Savevski1Maddalena Menini2Alessandro Schilirò3Gianluca Franchellucci4Arianna Dal Buono5Cristina Bezzio6Roberto Gabbiadini7Cesare Hassan8Alessandro Repici9Alessandro Armuzzi10IRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyIRCCS Humanitas Research Hospital-Via Manzoni 56, Rozzano, 20089 Milan, ItalyResearch of Inflammatory Bowel Disease (IBD) involves integrating diverse and heterogeneous data sources, from clinical records to imaging and laboratory results, which presents significant challenges in data harmonization and exploration. These challenges are also reflected in the development of machine-learning applications, where inconsistencies in data quality, missing information, and variability in data formats can adversely affect the performance and generalizability of models. In this study, we describe the collection and curation of a comprehensive dataset focused on IBD. In addition, we present a dedicated research platform. We focus on ethical standards, data protection, and seamless integration of different data types. We also discuss the challenges encountered, as well as the insights gained during its implementation.https://www.mdpi.com/2306-5729/10/7/100data integrationresearch platformmedical information systems |
| spellingShingle | Aldo Marzullo Victor Savevski Maddalena Menini Alessandro Schilirò Gianluca Franchellucci Arianna Dal Buono Cristina Bezzio Roberto Gabbiadini Cesare Hassan Alessandro Repici Alessandro Armuzzi Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort Data data integration research platform medical information systems |
| title | Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort |
| title_full | Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort |
| title_fullStr | Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort |
| title_full_unstemmed | Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort |
| title_short | Collecting and Analyzing IBD Clinical Data for Machine-Learning: Insights from an Italian Cohort |
| title_sort | collecting and analyzing ibd clinical data for machine learning insights from an italian cohort |
| topic | data integration research platform medical information systems |
| url | https://www.mdpi.com/2306-5729/10/7/100 |
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