Vol. 14 No. 2 (2023)

Neurodegenerative clinical records analyzer: detection of recurrent patterns within clinical records towards the identification of typical signs of neurodegenerative disease history

Erika Pasceri
University of Calabria
Mérième Bouhandi
University of Nantes
Claudia Lanza
University of Calabria
Anna Perri
University of Calabria
Valentina Laganà
Association for Neurogenetic Research (ARN)
Raffaele Maletta
Regional Neurogenetic Centre, ASP
Raffaele Di Lorenzo
Regional Neurogenetic Centre, ASP
Amalia C. Bruni
Regional Neurogenetic Centre, ASP

Published 2023-05-15


  • Alzheimer,
  • Categorization,
  • Electronic health records (EHR),
  • Machine learning,
  • Semantic annotation

How to Cite

Pasceri, E., Bouhandi, M., Lanza, C., Perri, A., Laganà, V., Maletta, R., Di Lorenzo, R., & Bruni, A. C. (2023). Neurodegenerative clinical records analyzer: detection of recurrent patterns within clinical records towards the identification of typical signs of neurodegenerative disease history. JLIS.It, 14(2), 20–38. https://doi.org/10.36253/jlis.it-522


When treating structured health-system-related knowledge, the establishment of an over-dimension to guide the separation of entities becomes essential. This is consistent with the information retrieval processes aimed at defining a coherent and dynamic way – meaning by that the multilevel integration of medical textual inputs and computational interpretation – to replicate the flow of data inserted in the clinical records. This study presents a strategic technique to categorize the clinical entities related to patients affected by neurodegenerative diseases. After a pre-processing range of tasks over paper-based and handwritten medical records, and through subsequent machine learning and, more specifically, natural language processing operations over the digitized clinical records, the research activity provides a semantic support system to detect the main symptoms and locate them in the appropriate clusters. Finally, the supervision of the experts proved to be essential in the correspondence sequence configuration aimed at providing an automatic reading of the clinical records according to the clinical data that is needed to predict the detection of neurodegenerative disease symptoms.


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