PHI Scrubber: A Deep Learning Approach
This work addresses privacy concerns in healthcare data management, but it appears incremental as it combines existing deep learning techniques with regular expressions for a specific application.
The paper tackles the problem of protecting patient privacy in electronic health records by proposing a deep learning system that identifies and removes personally identifiable information from physician notes, enabling secure data sharing for research and clinical trials.
Confidentiality of patient information is an essential part of Electronic Health Record System. Patient information, if exposed, can cause a serious damage to the privacy of individuals receiving healthcare. Hence it is important to remove such details from physician notes. A system is proposed which consists of a deep learning model where a de-convolutional neural network and bi-directional LSTM-CNN is used along with regular expressions to recognize and eliminate the individually identifiable information. This information is then removed from a medical practitioner's data which further allows the fair usage of such information among researchers and in clinical trials.