Optimizing annotation resources for natural language de-identification via a game theoretic framework.

ABSTRACT:

Objective

Electronic medical records (EMRs) are increasingly repurposed for activities beyond clinical care, such as to support translational research and public policy analysis. To mitigate privacy risks, healthcare organizations (HCOs) aim to remove potentially identifying patient information. A substantial quantity of EMR data is in natural language form and there are concerns that automated tools for detecting identifiers are imperfect and leak information that can be exploited by ill-intentioned data recipients. Thus, HCOs have been encouraged to invest as much effort as possible to find and detect potential identifiers, but such a strategy assumes the recipients are sufficiently incentivized and capable of exploiting leaked identifiers. In practice, such an assumption may not

SUBMITTER: Li M 

PROVIDER: S-EPMC4996128 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature