Деидентификация

task ID: deidentification

Related Papers

Role of Local Context in Automatic Deidentification of Ungrammatical, Fragmented Text

Authors: nan

Publication: TIPSTER (2006)

DOI: nan

Deidentification of clinical records is a crucial step before these records can be distributed to non-hospital researchers. Most approaches to deidentification rely heavily on dictionaries and heuristic rules; these approaches …...

24250

Influence of Module Order on Rule-Based De-identification of Personal Names in Electronic Patient Records Written in Swedish

Authors: nan

Publication: TIPSTER (2010)

DOI: nan

AbstractElectronic patient records (EPRs) are a valuable resource for research but for confidentiality reasons they cannot be used freely. In order to make EPRs available to a wider group of …...

8606

Adversarial Learning of Privacy-Preserving Text Representations for De-Identification of Medical Records

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractDe-identification is the task of detecting protected health information (PHI) in medical text. It is a critical step in sanitizing electronic health records (EHR) to be shared for research. Automatic …...

39382

A Semi-supervised Approach for De-identification of Swedish Clinical Text

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractAn abundance of electronic health records (EHR) is produced every day within healthcare. The records possess valuable information for research and future improvement of healthcare. Multiple efforts have been done …...

14437

Closing the Gap: Joint De-Identification and Concept Extraction in the Clinical Domain

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractExploiting natural language processing in the clinical domain requires de-identification, i.e., anonymization of personal information in texts. However, current research considers de-identification and downstream tasks, such as concept extraction, only …...

37352

CodE Alltag 2.0 — A Pseudonymized German-Language Email Corpus

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractThe vast amount of social communication distributed over various electronic media channels (tweets, blogs, emails, etc.), so-called user-generated content (UGC), creates entirely new opportunities for today’s NLP research. Yet, data …...

14403