Naïve Bayes classifiers with feature selection to predict hospitalization and complications due to objects swallowing and ingestion among European children

Safety Science

Volumn 51, Issue 1, 2013, Pages 1-5

  Berchialla, Paola ^a   Foltran, Francesca ^b   Gregori, Dario ^c  

^a UNIVERSITY OF TURIN   (Italy)

^b UNIVERSITY OF PISA   (Italy)

^c UNIVERSITY OF PADOVA   (Italy)

Author keywords

Aero digestive tract injuries; Children; Consumer safety; Foreign Bodies injuries; Toys

Indexed keywords

AERO-DIGESTIVE TRACT INJURIES; CHILDREN; CONSUMER SAFETY; FOREIGN BODIES INJURIES; TOYS;

AUTOMOBILE MANUFACTURE; CLASSIFICATION (OF INFORMATION); CONSUMER PRODUCTS; DIGESTIVE SYSTEM; PEDIATRICS; PRODUCT DESIGN;

HOSPITALS;

ACCURACY; ADOLESCENT; ADVERSE OUTCOME; ARTICLE; BAYESIAN LEARNING; CHILD; CHILD BEHAVIOR; CHILD HOSPITALIZATION; CHILD SAFETY; CLASSIFICATION ALGORITHM; CLINICAL EFFECTIVENESS; CONTROLLED STUDY; DEVICE SAFETY; EUROPE; FEMALE; FOREIGN BODY ASPIRATION; HUMAN; INFANT; INJURY SEVERITY; MAJOR CLINICAL STUDY; MALE; PATIENT ASSESSMENT; PREDICTION; PRIORITY JOURNAL;

EID: 84865439735     PISSN: 09257535     EISSN: 18791042     Source Type: Journal    
DOI: 10.1016/j.ssci.2012.05.021     Document Type: Article

References (24)

1. Berchialla P., Snidero S., et al. Predicting severity of foreign body injuries in children in upper airways: an approach based on regression trees. Risk Anal. 2007, 27(5):1255-1263.
2. Blanco R., Inza I., et al. Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. J. Biomed. Inform. 2005, 38(5):376-388.
3. Draper D., Fouskakis D. A case study of stochastic optimization in health policy: problem formulation and preliminary results. J. Global Optimiz. 2000, 18(4):399-416.
4. Glick M., Klon A.E., et al. Enrichment of extremely noisy high-throughput screening data using a naïve Bayes classifier. J. Biomol. Screen 2004, 9(1):32-36.
5. Gregori D. Preventing foreign body injuries in children: a key role to play for the injury community. Injury Prevent. 2008, 14(6):411.
6. Gregori D., Snidero S., et al. Foreign body causing and prolonging hospitalization in children 0-14age: early results from the ESFBI Study. Healthcare System and Patient Safety 2005, 101-105. Taylor & Francis, Florence, Italy. R. Tartaglia, S. Bagnara, T. Bellandi, S. Albolino (Eds.).
7. Harrell F.E., Lee K.L., et al. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat. Med. 1996, 15(4):361-387.
8. Higo R., Matsumoto Y., et al. Foreign bodies in the aerodigestive tract in pediatric patients. Auris Nasus Larynx 2003, 30(4):397-401.
9. Jelonek J., Stefanowski J. Feature subset selection for classification of histological images. Artif. Intell. Med. 1997, 9(3):227-239.
10. Kononenko I. Comparison of inductive and naïve Bayesian learning approaches to automatic knowledge acquisition. Current Trends in Knowledge Acquisition 1990, IOS Press, Amsterdam. B. Wielinga, J. Boose, B. Gaines, G. Shereiber, M. van Someren (Eds.).
11. Kononenko I. Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 2001, 23(1):89-109.
12. Michalski R., Kaufman K. Learning patterns in noisy data: the AQ approach. Machine Learning and its Applications 2001, vol. viii:324. Springer, Berlin; London.
13. Michalski R.S., Bratko I., et al. Machine learning and data mining: methods and applications 1998, Wiley, Chichester.
14. Milkovich S.M., Rider G., et al. Application of data for prevention of foreign body injury in children. Int. J. Pediatr. Otorhinolaryngol. 2003, 67(Suppl 1):S193-196.
15. Milkovich S.M., Altkorn R., et al. Development of the small parts cylinder: lessons learned. Laryngoscope 2008, 118(11):2082-2086.
16. Moler E.J., Radisky D.C., et al. Integrating naïve Bayes models and external knowledge to examine copper and iron homeostasis in S. cerevisiae. Physiol. Genomics 2000, 4(2):127-135.
17. National Center for Health Statistics, 2002. Classification of Diseases and Injuries. from ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Publications/ICD-9/ucod.txt.
18. Nordyke R., Kulikowski C. A comparison of methods for the automated diagnosis of thyroid dysfunction. Comput. Biomed. Res. 1971, 4:374-389.
19. R Development Core Team, 2008. R: a language and environment for statistical computing. Vienna, Austria.
20. Rider G., Milkovich S., et al. Quantitative risk analysis. Injury Control Safety Promot. 2000, 7(2):115-133.
21. Rimell F.L., Thome A., et al. Characteristics of objects that cause choking in children. J. Am. Med. Assoc. 1996, 274(22):1763-1766.
22. Steyerberg E.W., Bleeker S.E., et al. Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J. Clin. Epidemiol. 2003, 56(5):441-447.
23. Sun H. A naïve Bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. J. Med. Chem. 2005, 48(12):4031-4039.
24. Yang J., Nugroho A.S., et al. Efficacy of interferon treatment for chronic hepatitis C predicted by feature subset selection and support vector machine. J. Med. Syst. 2007, 31(2):117-123.