Name
Abstract | ||
|---|---|---|
Objectives This article summarizes past and current data mining activities at the United States Food and Drug Administration (FDA). Target audience We address data miners in all sectors, anyone interested in the safety of products regulated by the FDA (predominantly medical products, food, veterinary products and nutrition, and tobacco products), and those interested in FDA activities. Scope Topics include routine and developmental data mining activities, short descriptions of mined FDA data, advantages and challenges of data mining at the FDA, and future directions of data mining at the FDA. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1093/jamia/ocv063 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION |
Keywords | Field | DocType |
data mining,pharmacovigilance,disproportionality analysis | Data mining,Target audience,Pharmacovigilance,Medicine,Drug administration | Journal |
Volume | Issue | ISSN |
23 | 2 | 1067-5027 |
Citations | PageRank | References |
4 | 0.46 | 4 |
Authors | ||
30 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| hesha j duggirala | 1 | 4 | 0.46 |
| joseph m tonning | 2 | 4 | 0.46 |
| ella smith | 3 | 4 | 0.46 |
| roselie a bright | 4 | 4 | 0.46 |
| j d baker | 5 | 4 | 0.46 |
| r w ball | 6 | 4 | 0.46 |
| carlos bell | 7 | 4 | 0.46 |
| susan j brightponte | 8 | 4 | 0.46 |
| Taxiarchis Botsis | 9 | 99 | 10.86 |
| khaled bouri | 10 | 4 | 0.46 |
| marc boyer | 11 | 4 | 0.46 |
| keith k burkhart | 12 | 4 | 0.46 |
| g steven condrey | 13 | 4 | 0.46 |
| james j chen | 14 | 4 | 0.46 |
| stuart j chirtel | 15 | 4 | 0.46 |
| ross filice | 16 | 4 | 0.46 |
| henry francis | 17 | 4 | 0.46 |
| hongying jiang | 18 | 4 | 0.46 |
| jonathan g levine | 19 | 5 | 0.81 |
| david c martin | 20 | 4 | 0.46 |
| taiye oladipo | 21 | 4 | 0.46 |
| rene o neill | 22 | 4 | 0.46 |
| lee anne m palmer | 23 | 4 | 0.46 |
| antonio paredes | 24 | 4 | 0.46 |
| george rochester | 25 | 4 | 0.46 |
| deborah sholtes | 26 | 4 | 0.46 |
| Ana Szarfman | 27 | 11 | 1.64 |
| huilee wong | 28 | 4 | 0.46 |
| zhiheng xu | 29 | 4 | 0.46 |
| taha kasshout | 30 | 4 | 0.46 |