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Literature DB >> 16970507

What counts in data mining?

Manfred Hauben¹, Vaishali K Patadia, David Goldsmith.

Abstract

Mesh：

Year: 2006 PMID： 16970507 DOI： 10.2165/00002018-200629100-00001

Source DB: PubMed Journal: Drug Saf ISSN： 0114-5916 Impact factor: 5.606

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22 in total

1. Comparison of data mining methodologies using Japanese spontaneous reports.

Authors: Kiyoshi Kubota; Daisuke Koide; Toshiki Hirai
Journal: Pharmacoepidemiol Drug Saf Date: 2004-06 Impact factor: 2.890

2. Data mining in pharmacovigilance: the need for a balanced perspective.

Authors: Manfred Hauben; Vaishali Patadia; Charles Gerrits; Louisa Walsh; Lester Reich
Journal: Drug Saf Date: 2005 Impact factor: 5.606

3. Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance.

Authors: Corneliu Henegar; Cédric Bousquet; Agnès Lillo-Le Louët; Patrice Degoulet; Marie-Christine Jaulent
Journal: Comput Biol Med Date: 2005-09-26 Impact factor: 4.589

4. Comparing data mining methods on the VAERS database.

Authors: David Banks; Emily Jane Woo; Dale R Burwen; Phil Perucci; M Miles Braun; Robert Ball
Journal: Pharmacoepidemiol Drug Saf Date: 2005-09 Impact factor: 2.890

5. Valproate-induced parkinsonism: use of a newer pharmacovigilance tool to investigate the reporting of an unanticipated adverse event with an "old" drug.

Authors: Manfred Hauben; Lester Reich
Journal: Mov Disord Date: 2005-03 Impact factor: 10.338

6. Appraisal of the MedDRA conceptual structure for describing and grouping adverse drug reactions.

Authors: Cédric Bousquet; Georges Lagier; Agnès Lillo-Le Louët; Christine Le Beller; Alain Venot; Marie-Christine Jaulent
Journal: Drug Saf Date: 2005 Impact factor: 5.606

Review 7. Data mining in spontaneous reports.

Authors: Andrew Bate; I R Edwards
Journal: Basic Clin Pharmacol Toxicol Date: 2006-03 Impact factor: 4.080

8. Sirolimus-induced thrombotic microangiopathy is associated with decreased expression of vascular endothelial growth factor in kidneys.

Authors: Hervé Sartelet; Olivier Toupance; Marianne Lorenzato; Fouad Fadel; Laure Hélène Noel; Eymeric Lagonotte; Philippe Birembaut; Jacques Chanard; Philippe Rieu
Journal: Am J Transplant Date: 2005-10 Impact factor: 8.086

9. Sirolimus-associated heavy proteinuria in a renal transplant recipient: evidence for a tubular mechanism.

Authors: L Straathof-Galema; J F M Wetzels; H B P M Dijkman; E J Steenbergen; L B Hilbrands
Journal: Am J Transplant Date: 2006-02 Impact factor: 8.086

10. Wound healing complications with de novo sirolimus versus mycophenolate mofetil-based regimen in cardiac transplant recipients.

Authors: S Kuppahally; A Al-Khaldi; D Weisshaar; H A Valantine; P Oyer; R C Robbins; S A Hunt
Journal: Am J Transplant Date: 2006-05 Impact factor: 8.086

6 in total

What counts in data mining?

1. Comparison of data mining methodologies using Japanese spontaneous reports.

2. Data mining in pharmacovigilance: the need for a balanced perspective.

3. Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance.

4. Comparing data mining methods on the VAERS database.

5. Valproate-induced parkinsonism: use of a newer pharmacovigilance tool to investigate the reporting of an unanticipated adverse event with an "old" drug.

6. Appraisal of the MedDRA conceptual structure for describing and grouping adverse drug reactions.

Review 7. Data mining in spontaneous reports.

8. Sirolimus-induced thrombotic microangiopathy is associated with decreased expression of vascular endothelial growth factor in kidneys.

9. Sirolimus-associated heavy proteinuria in a renal transplant recipient: evidence for a tubular mechanism.

10. Wound healing complications with de novo sirolimus versus mycophenolate mofetil-based regimen in cardiac transplant recipients.

1. Terminological challenges in safety surveillance.

2. Potential use of data-mining algorithms for the detection of 'surprise' adverse drug reactions.

3. Illusions of objectivity and a recommendation for reporting data mining results.

4. Evaluation of automated term groupings for detecting anaphylactic shock signals for drugs.

5. The impact of database restriction on pharmacovigilance signal detection of selected cancer therapies.

6. An evaluation of three signal-detection algorithms using a highly inclusive reference event database.