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Federica Mandreoli
Researcher at University of Modena and Reggio Emilia
Publications - 147
Citations - 1821
Federica Mandreoli is an academic researcher from University of Modena and Reggio Emilia. The author has contributed to research in topics: XML & Semantic Web. The author has an hindex of 22, co-authored 142 publications receiving 1639 citations. Previous affiliations of Federica Mandreoli include University of Manchester & University of Bologna.
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Proceedings Article
Data-Driven, AI-Based Clinical Practice: Experiences, Challenges, and Research Directions
TL;DR: An overview of the main research challenges that need to be addressed to design and implement data–driven healthcare applications is provided and a research agenda that outlines the future of research in this field is proposed.
Disambiguation of Structure-Based Information in the STRIDER System
TL;DR: The current version of STRIDER is presented, a versatile system for the disambiguation of structure-based information like XML schemas, structures of XML documents and web directories that can be of support to the semantic-awareness of a wide range of applications, thanks to its novel and fully-automated disambIGuation algorithms.
Semantic Web Service Composition in the NeP4B Project: Challenges and Architectural Issues
TL;DR: This paper proposes an alternative peer to peer architecture based on the NeP4B project, which does not well cope with the scalability and flexibility requirements of dynamic, fast changing contexts.
Exploiting related digital library corpora with query rewriting.
TL;DR: The method proposed involves a preliminary schema matching process, which automatically identifies the semantic and structural similarities between the schema elements to be used in the subsequent operation of query rewriting, in which a query written on a source schema is automatically rewritten in order to be compatible with the other useful XML documents.
Proceedings Article
Predicting respiratory failure in patients with COVID-19 pneumonia: A case study from Northern Italy
TL;DR: In this paper, a machine learning model was developed to estimate the probability that a patient admitted to hospital with COVID-19 symptoms would develop severe respiratory failure and require intensive care within 48 hours of admission.