SemOntoMap: A Hybrid Approach for Semantic Annotation of Clinical Texts.

Journal: Studies in health technology and informatics
Published Date:

Abstract

This study addresses the challenge of leveraging free-text descriptions in Electronic Health Records (EHR) for clinical research and healthcare improvement. Despite the potential of this data, its direct interpretation by computers is limited. Semantic annotation emerges as a method to make EHR free text machine-interpretable but struggles with specific domain ontologies and faces heightened difficulties in psychiatry. To tackle these challenges, this study proposes a system based on unsupervised learning techniques to extract entities and their relationships, aligning them with a domain ontology. The effectiveness of this system has been validated within PsyCARE project by analyzing 60 patient discharge summaries.

Authors

  • Ons Aouina
    Sorbonne Université, Inserm, Université Sorbonne Paris-Nord, LIMICS, Paris, France.
  • Jacques Hilbey
    Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS), UMRS_1142, Paris, France. Electronic address: jacques.hilbey@inserm.fr.
  • Jean Charlet
    INSERM, U1142, LIMICS, F-75006, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR_S 1142, LIMICS, F-75006, Paris, France; Université Paris 13, Sorbonne Paris Cité, LIMICS.