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

Danila AzzolinaSilvia BressanGiulia LorenzoniGiulia Andrea BaldanPatrizia BartolottaFederico ScognamiglioAndrea FrancavillaCorrado LaneraLiviana Da DaltDario Gregori
Published in: JMIR public health and surveillance (2023)
This study shows that MLTs are promising techniques for improving epidemiological surveillance, allowing for the automatic classification of pediatric ED free-text diagnoses. The MLTs revealed a suitable classification performance, especially for general injuries and intentional injury classification. This automatic classification could facilitate the epidemiological surveillance of pediatric injuries by also reducing the health professionals' efforts in manually classifying diagnoses for research purposes.
Keyphrases
  • machine learning
  • deep learning
  • emergency department
  • artificial intelligence
  • public health
  • big data
  • smoking cessation
  • single cell
  • quality improvement
  • neural network