Exploring Negated Entites for Named Entity Recognition in Italian Lung Cancer Clinical Reports.

Journal: Studies in health technology and informatics
Published Date: May 23, 2024

Abstract

This paper explores the potential of leveraging electronic health records (EHRs) for personalized health research through the application of artificial intelligence (AI) techniques, specifically Named Entity Recognition (NER). By extracting crucial patient information from clinical texts, including diagnoses, medications, symptoms, and lab tests, AI facilitates the rapid identification of relevant data, paving the way for future care paradigms. The study focuses on Non-small cell lung cancer (NSCLC) in Italian clinical notes, introducing a novel set of 29 clinical entities that include both presence or absence (negation) of relevant information associated with NSCLC. Using a state-of-the-art model pretrained on Italian biomedical texts, we achieve promising results (average F1-score of 80.8%), demonstrating the feasibility of employing AI for extracting biomedical information in the Italian language.

Authors

  • Domenico Paolo
    Unit of Computer Systems & Bioinformatics, Università Campus Bio-Medico di Roma, Italy.
  • Alessandro Bria
    Department of Engineering, University Campus Bio-Medico of Rome, Rome, Italy.
  • Carlo Greco
    Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Italy.
  • Marco Russano
    Operative Research Unit of Medical Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, Rome, Italy; Department of Surgery and Cancer, Hammersmith Hospital Campus, Imperial College London, London, United Kingdom.
  • Sara Ramella
    Operative Research Unit of Radiation Oncology, Fondazione Policlinico Universitario Campus Bio-Medico, Italy.
  • Paolo Soda
    Unit of Computer Systems and Bioinformatics, Department of Engineering, University Campus Bio-Medico of Rome, Italy; Department of Radiation Sciences, Radiation Physics, Biomedical Engineering, Umeå, University, Umeå, Sweden. Electronic address: paolo.soda@umu.se.
  • Rosa Sicilia
    Unit of Computer Systems & Bioinformatics, Università Campus Bio-Medico di Roma, Italy.

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