Does BERT Pretrained on Clinical Notes Reveal Sensitive Data?
This addresses privacy risks for patients and healthcare institutions when sharing pretrained models derived from sensitive electronic health records, though it is incremental as it builds on existing probing techniques.
The study investigated whether BERT models pretrained on clinical notes could leak sensitive patient data, finding that simple probing methods failed to extract meaningful personal health information from the MIMIC-III corpus, but more sophisticated attacks might succeed.
Large Transformers pretrained over clinical notes from Electronic Health Records (EHR) have afforded substantial gains in performance on predictive clinical tasks. The cost of training such models (and the necessity of data access to do so) coupled with their utility motivates parameter sharing, i.e., the release of pretrained models such as ClinicalBERT. While most efforts have used deidentified EHR, many researchers have access to large sets of sensitive, non-deidentified EHR with which they might train a BERT model (or similar). Would it be safe to release the weights of such a model if they did? In this work, we design a battery of approaches intended to recover Personal Health Information (PHI) from a trained BERT. Specifically, we attempt to recover patient names and conditions with which they are associated. We find that simple probing methods are not able to meaningfully extract sensitive information from BERT trained over the MIMIC-III corpus of EHR. However, more sophisticated "attacks" may succeed in doing so: To facilitate such research, we make our experimental setup and baseline probing models available at https://github.com/elehman16/exposing_patient_data_release