We have been building a proof-of-concept tool that scores the privacy risk of free text healthcare data. To use our tool effectivly, users need a basic understanding of the entities within their dataset which may contribute to privacy risk.
There are various tools for annotating and exploring free text data. The author explores some of these tools and discusses his experiences.
Over recent years, larger, more data-intensive Language Models (LMs) with greatly enhanced performance have been developed. The enhanced functionality has driven widespread interest in adoption of LMs in Healthcare, owing to the large amounts of unstructured text data generated within healthcare pathways.
However, with this heightened interest, it becomes critical to comprehend the inherent privacy risks associated with these LMs, given the sensitive nature of Healthcare data. This PhD Internship project sought to understand more about the Privacy-Risk Landscape for healthcare LMs through a literature review and exploration of some technical applications.