Benjamin Wellner

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: The de-identification tool achieves high accuracy when training and test sets are homogeneous (ie, both real or resynthesized records), but the resynthesis component regularizes the data to make them less "realistic," resulting in loss of performance particularly when training on resynthesesized data and testing on real data.

TL;DR: The human annotation effort needed to produce a system that de-identifies at high accuracy using the MIST framework is quantified, suggesting that the wider variety and contexts for protected health information in social work notes is more difficult to model.

TL;DR: A challenge task for the second BioCreAtIvE (Critical Assessment of Information Extraction in Biology) that requires participating systems to provide lists of the EntrezGene (formerly LocusLink) identifiers for all human genes and proteins mentioned in a MEDLINE abstract is developed.

TL;DR: The Coherent Data Set is a novel synthetic data set that leverages structured data from Synthea™ to create a longitudinal, “coherent” patient-level electronic health record (EHR).