Two Methods to De-identify Large Patient Datasets Greatly Reduced the Risk of Re-identification

PHILADELPHIA — Two de-identification methods, k-anonymization and adding a “fuzzy factor,” significantly reduced the risk of re-identification of patients in a dataset of 5 million patient records from a large cervical cancer screening program in Norway, according to results published in Cancer Epidemiology, Biomarkers & Prevention, a journal of the American Associ​ation for Cancer Research. “Researchers typically get […]

The post Two Methods to De-identify Large Patient Datasets Greatly Reduced the Risk of Re-identification appeared first on Healthcanal.com.

Big-Data Analysis Points Toward New Drug Discovery Method

New Tool May Identify Treatments for Cancer, Other Diseases By Laura Kurtzman A research team led by scientists at UC San Francisco has developed a computational method to systematically probe massive amounts of open-access data to discover new ways to use drugs, including some that have already been approved for other uses. The method enables scientists […]

The post Big-Data Analysis Points Toward New Drug Discovery Method appeared first on Healthcanal.com.