Characterizing mammography reports for health analytics

Abstract

As massive collections of digital health data are becoming available, the opportunities for large scale automated analysis increase. In particular, the widespread collection of detailed health information is expected to help realize a vision of evidence-based public health and patient-centric health care. Within such a framework for large scale health analytics we describe several methods to characterize and analyze free-text mammography reports, including their temporal dimension, using information retrieval, supervised learning, and classical statistical techniques. We present experimental results with a large collection of mostly unlabeled reports that demonstrate the validity and usefulness of the approach, since these results are consistent with the known features of the data and provide novel insights about it.