Dr. Xiaoqian Jiang: Sensitive Data Detection with High-Throughput Machine Learning Models in Electronic Health Records
Every month, ETAI will invite a speaker to participate in a monthly speaker series related to ethical issues in biomedical research. The monthly speaker series is part of our module's milestones relating to engagement and reflection on ethics with the AI-READI team.
This month, Dr. Jiang explored how a groundbreaking discovery was utilized to generate 30 metadata-based features through machine learning for the automatic detection of PHI fields in structured Electronic Health Record (EHR) data. The model, trained on diverse EHR databases, attained an impressive 99% accuracy in identifying PHI-related fields for unseen datasets, presenting significant implications for industries dealing with sensitive data.You can find a recording to his talk here