Detection of adverse drug events: Proposal of a data model

Studies in Health Technology and Informatics

Volumn 148, Issue , 2009, Pages 63-74

  Chazard, Emmanuel ^a   Merlin, Béatrice ^a   Ficheur, Grégoire ^a   Sarfati, Jean Charles ^b   Beuscart, Régis ^a  

^a LILLE UNIVERSITY HOSPITAL   (France)

^b Oracle France SAS   (France)

Author keywords

Adverse drug event detection; Data model; Data mining; Interoperability

Indexed keywords

DATA STRUCTURES; HOSPITALS; HUMAN ENGINEERING; INTEROPERABILITY;

ADVERSE DRUG EVENTS; DATA MINING PROCESS; DRUG PRESCRIPTIONS; ELECTRONIC HEALTH RECORD (EHRS);

DATA MINING;

EID: 71749085101     PISSN: 09269630     EISSN: 18798365     Source Type: Book Series    
DOI: 10.3233/978-1-60750-043-8-63     Document Type: Conference Paper

References (20)

1. Morimoto T, Gandhi TK, Seger AC, Hsieh TC, Bates DW. Adverse drug events and medication errors: detection and classification methods. Qual Saf Health Care. 2004 Aug;13(4):306-14.
2. Murff HJ, Patel VL, Hripcsak G, Bates DW. Detecting adverse events for patient safety research: a review of current methodologies. J Biomed Inform. 2003 Feb-Apr;36(1-2):131-43.
3. Gurwitz JH, Field TS, Harrold LR, Rothschild J, Debellis K, Seger AC, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003 Mar 5;289(9):1107-16.
4. Jalloh OB, Waitman LR. Improving Computerized Provider Order Entry (CPOE) usability by data mining users' queries from access logs. AMIA Annu Symp Proc. 2006:379-83.
5. Zhang HP, Crowley J, Sox H, Olshen RA. Tree structural statistical methods. Encyclopedia of Biostatistics. Chichester, England: Wiley; 2001. p. 4561-73.
6. Breiman L. Classification and regression trees. Belmont, Calif.: Wadsworth International Group; 1984.
7. Fayyad U, Piatetsky-Shapiro G, Smyth P, editors. From data mining to knowledge discovery: an overview. 2nd Int Conf on Knowledge Discovery and Data Mining; 1996.
8. Lavrac N. Selected techniques for data mining in medicine. Artif Intell Med. 1999 May;16(1):3-23.
9. Quinlan JR. Introduction of Decision Trees. Machine Learning. 1986;1:81-106.
10. Ripley BD. Pattern recognition and neural networks. Cambridge; New York: Cambridge University Press; 1996.
11. Agrawal R, Imielinski T, Swami A, editors. Mining Association Rules between Sets of Items in Large Databases. Proceedings of the ACM SIGMOD International Conference on Management of Data; 1993 May. Washington D.C.
12. Piatetsky-Shapiro G, Frawley W. Knowledge discovery in databases. Menlo Park, Calif.: AAAI Press: MIT Press; 1991.
13. International Classification of Diseases. [cited 2009 february 24]; Available from: http://www.who.int/classifications/icd/en.
14. Anatomical and Therapeutical Classification. [cited 2009 february 24]; Available from: http://www.whocc.no/atcddd.
15. International Union of Pure and Applied Chemistry. [cited 2009 february 24]; Available from: http://www.iupac.org.
16. Health level 7. [cited 2009 April 21]; Available from: http://www.hl7.org/.
17. Chazard E, Preda C, Merlin B, Ficheur G, Beuscart R. Data-mining-based detection of adverse drug events. Stud Health Technol Inform. 2009; [IN PRESS].
18. European Research Council. [cited 2009 february 24]; Available from: http://erc.europa.eu.
19. Seventh Framework programme. [cited 2009 february 24]; Available from: http://cordis.europa.eu/fp7/home-en.html.
20. Patient Safety by Intelligent Procedures in medication. [cited 2009 february 24]; Available from: http://www.psip-project.eu.