Francesca Parisi

Research Abstract

Data interoperability in healthcare processes is necessary to exchange meaningful information among healthcare professionals and Institutions. Classification and coding systems such as the LOINC international standard for names and codes allows for information exchange while maintaining the intrinsic semantic value conveyed by the code unaltered. All clinical laboratories use their local linguistic forms to identify specific clinical observations. Each biological or clinical test is described using terms, codes or acronyms valid only in the local context thus creating issues during the mapping operations to the official international standard LOINC definitions and codes. The methodology considered in my PhD research, aims to give a support for data semantic interoperability in the e‐Health domain. In particular, it concerns the construction of context-free syntactical grammars for automatic recognitions of local linguistic forms and detection of data correctness level for biological or clinical tests carried out in specific laboratories. My current research interests include the NLP application for automatic term and concept recognition and information extraction from specific domain structured or unstructured texts.