NLP-based removal of personally identifiable information from Hungarian electronic health records

The results indicate that similarly to other state-of-the-art systems our model is highly effective at identifying PII in clinical texts, guaranteeing that sensitive information in clinical documents can be protected without sacrificing the quality or usability of the data for research purposes. Despite these positive outcomes, several areas remain to be improved, such as the conduction of additional testing on diverse datasets, particularly those from different healthcare institutions. With ongoing refinements, these models have the potential to greatly enhance the efficiency of data de-identification processes, ensuring compliance with privacy regulations while promoting the secure sharing of medical data for scientific progress.