Electronic patient records are produced in abundance every day and there is a demand to
use them for research or management purposes. The records, however, contain information …

This article presents experiments with pseudonymised Swedish clinical text used as training
data to de-identify real clinical text with the future aim to transfer non-sensitive training data …

An abundance of electronic health records (EHR) is produced every day within healthcare.
The records possess valuable information for research and future improvement of …

Patient records contain valuable information in the form of both structured data and free text;
however this information is sensitive since it can reveal the identity of patients. In order to …

Electronic health records contain valuable information about symptoms, diagnosis, treatment
and outcomes of the treatments of individual patients. However, the records may also …

Privacy preservation of sensitive information is one of the main concerns in clinical text
mining. Due to the inherent privacy risks of handling clinical data, the clinical corpora used …

In order to analyse the information present in medical records while maintaining patient
privacy, there is a basic need for techniques to automatically de-identify the free text …

Unstructured information in electronic health records provide an invaluable resource for
medical research. To protect the confidentiality of patients and to conform to privacy …

Background In order to perform research on the information contained in Electronic Patient
Records (EPRs), access to the data itself is needed. This is often very difficult due to …

A major hurdle in the development of natural language processing (NLP) methods for
Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns …