Cheryl Clark

TL;DR: The open source MITRE Identification Scrubber Toolkit (MIST) provides an environment to support rapid tailoring of automated de-identification to different document types, using automatically learned classifiers to de-identified and protect sensitive information.

TL;DR: This work proposes that an optimizable solution does not equal a generalizable solution, and introduces a new machine learning-based Polarity Module for detecting negation in clinical text, and extensively compare its performance across domains.

TL;DR: A medical information extraction system that combines a rule-based extraction engine with machine learning algorithms to identify and categorize references to patient smoking in clinical reports and shows overall accuracy in the 90s on all data sets used.

TL;DR: Experimental results suggest approximately 90% of residual identifiers can be effectively concealed by the HIPS approach in text containing average and high densities of personal identifying information, which suggests HIPS is feasible, but requires further evaluation.

TL;DR: The goal of the work described here is to provide a light-weight, easy-to-use (small) set of terms ("Habitat-Lite") that captures high-level information about habitat while preserving a mapping to the recently launched Environment Ontology (EnvO).