Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/44467, first published .
Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach

Pediatric Injury Surveillance From Uncoded Emergency Department Admission Records in Italy: Machine Learning–Based Text-Mining Approach

Danila Azzolina   1 * , PhD ;   Silvia Bressan   2 * , PhD ;   Giulia Lorenzoni   3 , PhD ;   Giulia Andrea Baldan   3 , MA ;   Patrizia Bartolotta   3 , MD ;   Federico Scognamiglio   3 , MA ;   Andrea Francavilla   3 , MD ;   Corrado Lanera   3 , PhD ;   Liviana Da Dalt   2 * , MD ;   Dario Gregori   3 * , PhD

1 Department of Environmental and Preventive Sciences, University of Ferrara, Ferrara, Italy

2 Department of Women's and Children's Health, University of Padova, Padua, Italy

3 Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences, and Public Health, University of Padova, Padua, Italy

*these authors contributed equally

Corresponding Author:

  • Dario Gregori, PhD
  • Unit of Biostatistics, Epidemiology and Public Health
  • Department of Cardiac, Thoracic, Vascular Sciences, and Public Health
  • University of Padova
  • Via Leonardo Loredan 18
  • Padua, 35128
  • Italy
  • Phone: 39 049 8275384
  • Email: dario.gregori@unipd.it