Catálogo de publicaciones - libros
Advances in Knowledge Discovery and Data Mining: 10th Pacific-Asia Conference, PAKDD 2006, Singapore, April 9-12, 2006, Proceedings
Wee-Keong Ng ; Masaru Kitsuregawa ; Jianzhong Li ; Kuiyu Chang (eds.)
En conferencia: 10º Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) . Singapore, Singapore . April 9, 2006 - April 12, 2006
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
No disponibles.
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | 2006 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-3-540-33206-0
ISBN electrónico
978-3-540-33207-7
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2006
Información sobre derechos de publicación
© Springer-Verlag Berlin Heidelberg 2006
Tabla de contenidos
doi: 10.1007/11731139_101
Mining Unexpected Associations for Signalling Potential Adverse Drug Reactions from Administrative Health Databases
Huidong Jin; Jie Chen; Chris Kelman; Hongxing He; Damien McAullay; Christine M. O’Keefe
Adverse reactions to drugs are a leading cause of hospitalisation and death worldwide. Most post-marketing Adverse Drug Reaction (ADR) detection techniques analyse spontaneous ADR reports which underestimate ADRs significantly. This paper aims to signal ADRs from administrative health databases in which data are collected routinely and are readily available. We introduce a new knowledge representation, Unexpected Temporal Association Rules (UTARs), to describe patterns characteristic of ADRs. Due to their and , existing techniques cannot perform effectively. To handle this unexpectedness we introduce a new interestingness measure, , and give a user-based exclusion technique for its calculation. Combining it with an event-oriented data preparation technique to handle infrequency, we develop a new algorithm, MUTARA, for mining simple UTARs. MUTARA effectively short-lists some known ADRs such as the disease esophagitis unexpectedly associated with the drug alendronate. Similarly, MUTARA signals atorvastatin followed by nizatidine or dicloxacillin which may be prescribed to treat its side effects stomach ulcer or urinary tract infection, respectively. Compared with association mining techniques, MUTARA signals potential ADRs more effectively.
- Innovative Applications | Pp. 867-876