Citation

Valent F, Clagnan E, Zanier L. Epidemiol. Prev. 2014; 38(2): 116-122.

Vernacular Title

Classificazione delle cause traumatiche di accesso al pronto soccorso in Friuli Venezia Giulia mediante Naïve Bayes Classification.

Affiliation

Servizio di epidemiologia, Direzione centrale salute, integrazione sociosanitaria e politiche sociali, Regione autonoma Friuli Venezia Giulia francesca.valent@regione.fvg.it.

Copyright

(Copyright © 2014, Cooperativa Epidemiologia E Prevenzione)

DOI

unavailable

PMID

24986410

Abstract

OBJECTIVES: to assess whether Naïve Bayes Classification could be used to classify injury causes from the Emergency Room (ER) database, because in the Friuli Venezia Giulia Region (Northern Italy) the electronic ER data have never been used to study the epidemiology of injuries, because the proportion of generic "accidental" causes is much higher than that of injuries with a specific cause.

DESIGN: application of the Naïve Bayes Classification method to the regional ER database. SETTING AND PARTICIPANTS: 1997-2008 deaths for accidents by citizenship ("Italians" and "Immigrants" from Countries with strong migratory pressure or PFPM) in residents in Tuscany. MAIN OUTCOME MEASURES: sensitivity, specificity, positive and negative predictive values, agreement, and the kappa statistic were calculated for the train dataset and the distribution of causes of injury for the test dataset.

RESULTS: on 22.248 records with known cause, the classifications assigned by the model agreed moderately (kappa =0.53) with those assigned by ER personnel. The model was then used on 76.660 unclassified cases. Although sensitivity and positive predictive value of the method were generally poor, mainly due to limitations in the ER data, it allowed to estimate for the first time the frequency of specific injury causes in the Region.

CONCLUSION: the model was useful to provide the "big picture" of non-fatal injuries in the Region. To improve the collection of injury data at the ER, the options available for injury classification in the ER software are being revised to make categories exhaustive and mutually exclusive.

Language: it