A
Arianna Dagliati
Researcher at University of Manchester
Publications - 67
Citations - 1108
Arianna Dagliati is an academic researcher from University of Manchester. The author has contributed to research in topics: Medicine & Computer science. The author has an hindex of 15, co-authored 49 publications receiving 662 citations. Previous affiliations of Arianna Dagliati include University of Amsterdam & University of Pavia.
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Journal ArticleDOI
Machine Learning Methods to Predict Diabetes Complications
Arianna Dagliati,Simone Marini,Lucia Sacchi,Giulia Cogni,Marsida Teliti,Valentina Tibollo,Pasquale De Cata,Luca Chiovato,Riccardo Bellazzi +8 more
TL;DR: Logistic Regression with stepwise feature selection is used to predict the onset of retinopathy, neuropathy, or nephropathy, at different time scenarios, at 3, 5, and 7 years from the first visit at the Hospital Center for Diabetes.
Journal ArticleDOI
Integrated Multi-Omics Analyses in Oncology: A Review of Machine Learning Methods and Tools.
Giovanna Nicora,Francesca Vitali,Arianna Dagliati,Arianna Dagliati,Nophar Geifman,Riccardo Bellazzi +5 more
TL;DR: Recent data-driven methodologies that have been developed and applied to respond major challenges of stratified medicine in oncology, including patients' phenotyping, biomarker discovery, and drug repurposing are explored.
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Big Data Technologies: New Opportunities for Diabetes Management
TL;DR: The main concepts and definitions related to big data are reviewed, some efforts in health care are presented, and the potential role of big data in diabetes care is discussed.
Journal ArticleDOI
Health informatics and EHR to support clinical research in the COVID-19 pandemic: an overview.
TL;DR: In this paper, the authors focused their attention on collaborative data infrastructures to support COVID-19 research and on the open issues of data sharing and data governance that had made emerge.
Journal ArticleDOI
A dashboard-based system for supporting diabetes care.
Arianna Dagliati,Arianna Dagliati,Lucia Sacchi,Valentina Tibollo,Giulia Cogni,Marsida Teliti,Antonio Martinez-Millana,Vicente Traver,Daniele Segagni,Jorge Posada,Manuel Ottaviano,Giuseppe Fico,Maria Teresa Arredondo,Pasquale De Cata,Luca Chiovato,Riccardo Bellazzi +15 more
TL;DR: This study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle.