Corrado Lanera, Giulia Lorenzoni, Elisa Barbieri, Gianluca Piras, Arjun Magge, Davy Weissenbacher, Daniele Donà, Luigi Cantarutti, Graciela Gonzalez-Hernandez, Carlo Giaquinto, Dario Gregori
Free-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records. We analyzed the Pedianet database, which includes data from January 2004 to August 2017. The model categorizes narratives from clinical record diagnoses into six types: no otitis, non-media otitis, non-acute otitis media (OM), acute OM (AOM), AOM with perforation, and recurrent AOM...
December 25, 2023: Journal of Personalized Medicine