Privacy Risks in German Patient Forums: A NER-Based Approach to Enrich Digital Twins

The online sharing of personal health data by individuals has raised privacy concerns. This paper presents a Named Entity Recognition (NER)-based analysis to detect potential privacy risks in German patient forums. The objective is to extract sensitive information from user-generated texts and augment existing digital profiles of users to demonstrate the potential threats posed by the aggregation of information. To achieve this, we trained a NER model on a large corpus of German patient forum texts and evaluated its performance using standard metrics. The results show that the NER model can effectively extract health-related information from German texts with a micro-average precision of 0.8666, a recall of 0.9633 and an F1-score of 0.9124. This enables the creation of Digital Twins that accurately reflect the health-related characteristics of individuals. However, when this information is combined with data from different platforms, it poses a potential threat to users’ privacy and underlines the need to warn users.