Name
Abstract | ||
|---|---|---|
We present a technique for the automatic identification of clinically-relevant patterns in medical datasets. To preserve patient privacy, we propose and implement the idea of treating medical dataset as a black box for both internal and external users of data. The proposed approach directly handles clinical data queries on a given medical dataset, unlike the conventional approach of relying on the data de-identification process. Our integrated toolkit combines software engineering technologies such as Java EE and RESTful web services, which allows exchanging medical data in an unidentifiable XML format and restricts users to computed information. Existing techniques could make it possible for an adversary to succeed in data re-identification attempts by applying advanced computational techniques; therefore, we disallow the use of retrospective processing of data. We validate our approach on an endoscopic reporting application based on openEHR and MST standards. The implemented prototype system can be used to query datasets by clinical researchers, governmental or non-governmental organizations in monitoring health care services to improve quality of care. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-63194-3_15 | Lecture Notes in Computer Science |
DocType | Volume | ISSN |
Conference | 9062 | 0302-9743 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nafees Qamar | 1 | 52 | 7.57 |
| Yilong Yang | 2 | 13 | 4.09 |
| András Nádas | 3 | 0 | 0.34 |
| Zhiming Liu | 4 | 6 | 2.15 |
| Janos Sztipanovits | 5 | 1478 | 165.28 |