Tag: de-identification

Survey of machine learning-based approaches to de-identification of medical documents

Summer of Research project by Vithya Yogarajan, supervised by Michael Mayo, University of Waikato. Protecting patient confidentiality while using medical data from electronic health records (EHRs) for research is crucial. De-identification of EHR data provides the opportunity to use the data for research without the risk of breaching patient privacy, and avoids the need for …

Application of deep learning techniques in a de-identification system

Summer of Research project by Yicheng Shi, University of Auckland, supervised by Quentin Thurier (Orion Health). Clinical data, collected by healthcare providers when treating patients, is incredibly valuable for medical research – but good data is hard to get. This information can help researchers improve healthcare. For example, by studying a patient’s medical history, researchers …

Automated De-identification

Separating patients from their records The idea that a patient’s Electronic Health Record (EHR) has a secondary value, beyond the immediate treatment of a single individual, is becoming more prevalent as medical research incorporates machine learning (ML) practices. The information an EHR contains, such as symptoms, treatment plans, response to medication, can be extracted using …