DrugFusion - retrieval knowledge management for prediction of adverse drug events

Lecture Notes in Business Information Processing

Volumn 176 LNBIP, Issue , 2014, Pages 13-24

  Galushka, Mykola ^a   Gilani, Wasif ^a  

^a SAP UK Ltd   (United Kingdom)

Author keywords

Adverse drug events; Case based reasoning; Drug prescription

Indexed keywords

INFORMATION SYSTEMS; INFORMATION USE; KNOWLEDGE ACQUISITION; KNOWLEDGE MANAGEMENT; PATIENT TREATMENT;

ADAPTATION KNOWLEDGE; ADVERSE DRUG EVENTS; CASE BASED REASONING METHODOLOGIES; DRUG PRESCRIPTIONS; INTELLIGENT ASSISTANCES; MEDICAL PRACTITIONER; OPEN SOURCE FRAMEWORKS; UNITED STATES FOOD AND DRUG ADMINISTRATIONS;

CASE BASED REASONING;

EID: 84904562143     PISSN: 18651348     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-06695-0_2     Document Type: Conference Paper

References (13)

1. Ferner, R. E., McDowell, S. E.: Internet accounts of serious adverse drug reactions a study of experiences of stevens-johnson syndrome and toxic epidermal necrolysis. Can. J. Clin. Pharmacol. 8(2), 84-88 (2012).
2. Butt, T. F., Cox, A. R., Oyebode, J., Ferner, R. E.: Internet accounts of serious adverse drug reactions a study of experiences of stevens-johnson syndrome and toxic epidermal necrolysis. Drug Safety 35(12), 1159-1170 (2012).
3. Jin, H., Chen, J., He, H., Kelman, C., McAullay, D., O'Keefe, C. M.: Signaling potential adverse drug reactions from administrative health databases. IEEE Trans. Knowl. Data Eng. 22(6), 839-853 (2010).
4. Jin, H., Chen, J., He, H., Graham, W. J., Kelman, C., O'Keefe, C. M.: Mining unexpected temporal associations: Applications in detecting adverse drug reactions. IEEE Transactions on Information Technology in Biomedicine 12(4), 488-500 (2008).
5. Krska, J., Cox, A. R.: Adverse drug reactions. in: Clinical pharmacy and therapeutics. Clinical Pharmacology and Therapeutics 91, 467-474 (2012).
6. Kuan, J. K. P., Paul Fast, H. L.: k-nearest neighbour search for r-tree family. In: Proceedings on First International Conf. on Information, Communications, and Signal Processing, Singapore, pp. 924-928 (September 1997).
7. Galushka, M., Patterson, D. W., Nugent, C.: Assessment of four modifications of a novel indexing technique for case-based reasoning. Int. J. Intell. Syst. 22(4), 353-383 (2007).
8. Patterson, D. W., Rooney, N., Galushka, M.: Efficient similarity determination and case construction techniques for case-based reasoning. In: Craw, S., Preece, A. D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 292-305. Springer, Heidelberg (2002).
9. Schank, R.: Dynamic Memory: A Theory of Reminding and Learning in Computers and People. Cambridge University Press, New York (1983).
10. White, T.: Hadoop: The Definitive Guide. O'Reilly Media (2009).
11. Koutkias, V., Kilintzis, V., Stalidis, G., Lazou, K., Niès, J., Durand-Texte, L., McNair, P., Beuscart, R., Maglaveras, N.: Probability analysis on associations of adverse drug events with drug-drug interactions. BIBE 45(3), 1308-1312 (2007).
12. Koutkias, V., Kilintzis, V., Stalidis, G., Lazou, K., Niès, J., Durand-Texte, L., McNair, P., Beuscart, R., Maglaveras, N.: Knowledge engineering for adverse drug event prevention: On the design and development of a uniform, contextualized and sustainable knowledge-based framework. Journal of Biomedical Informatics 45(3), 495-506 (2012).
13. Ji, Y., Ying, H., Dews, P., Mansour, A., Tran, J., Miller, R. E., Massanari, R. M.: A potential causal association mining algorithm for screening adverse drug reactions in postmarketing surveillance. IEEE Transactions on Information Technology in Biomedicine 15(3), 428-437 (2011).