# name
KeyCARE: a framework for biomedical Keyword Extraction, term Categorization, and semantic Relation

# metadata.dc.description[].value
Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2023-2024. Tutor/Director: Juan Ignacio Barrios ; Director: Luis Gascó, Martin Krallinger

# metadata.dc.description.abstract[].value
The medical sector generates vast amounts of unstructured data, which, if processed correctly, can significantly enhance medical processes and their outcomes. This thesis presents the development of KeyCARE, a Python library for keyword extraction, term categorization, and relations extraction that tackles this need. Utilizing mainly unsupervised and few-shot methods, KeyCARE efficiently extracts classified keywords from medical records with a recall of up to 98% and an f-score of up to 61%, with partial overlaps considered as correct. While these scores are not comparable to those of supervised Named Entity Recognition systems, they set a high standard for an unsupervised alternative in scenarios of data scarcity. Moreover, the library incorporates relation extractors that identify hierarchical relationships among biomedical keywords and with terminologies, achieving a precision and recall of 93%. This has a clear application in terminology enrichment, data generation and information extraction, particularly in specific domains and low-resource languages such as Catalan. This thesis encompasses the comprehensive development of KeyCARE, including an in-depth evaluation of the implemented models as well as basic use cases demonstrating its practical applications.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Sergi Marsol Torrent, 2024

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Enginyeria Biomèdica

# metadata.dc.title[].value
KeyCARE: a framework for biomedical Keyword Extraction, term Categorization, and semantic Relation