Monitoring the Epidemiology of Otitis Using Free-Text Pediatric Medical Notes: A Deep Learning Approach.

ABSTRACT: 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. Utilizing deep learning architectures, including an ensemble model, this study addressed the challenges associated with the manual classification of extensive narrative data. The performance of the model was evaluated ac

SUBMITTER: Lanera C 

PROVIDER: S-EPMC10817419 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature