Name
Abstract | ||
|---|---|---|
In this paper, we present the problem of and research issues for anonymizing spontaneous ADE reporting data for privacy-preserving ADR signal detection. Three main characteristics of spontaneous ADE data are identified, including rare ADE events, multiple individual records, and no sensitive attribute but having quasi-sensitive attributes. We examine the feasibility of contemporary privacy-preserving models for anonymizing SRS datasets, showing their incompetence in handling the these issues and so arouse the need of new privacy models and data anonymizing methods. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/BIBM.2013.6732760 | 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
adverse drug event, privacy-preserving data publishing, ADR detection, spontaneous reporting system | Data science,World Wide Web,Computer science,Artificial intelligence,Publishing,Information privacy,Machine learning | Conference |
ISSN | Citations | PageRank |
2156-1125 | 1 | 0.38 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wen-Yang Lin | 1 | 399 | 35.72 |
| Duan-Chun Yang | 2 | 1 | 0.38 |