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Published in:

01-10-2011 | Original Paper

Characterizing Mammography Reports for Health Analytics

Authors: Carlos C. Rojas, Robert M. Patton, Barbara G. Beckerman

Published in: Journal of Medical Systems | Issue 5/2011

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 the transformation of a large data set of mostly unlabeled and free-text mammography data into a searchable and accessible collection, usable for analytics. We also describe several methods to characterize and analyze the data, including their temporal aspects, using information retrieval, supervised learning, and classical statistical techniques. We present experimental results that demonstrate the validity and usefulness of the approach, since the results are consistent with the known features of the data, provide novel insights about it, and can be used in specific applications. Additionally, based on the process of going from raw data to results from analysis, we present the architecture of a generic system for health analytics from clinical notes.

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Title: Characterizing Mammography Reports for Health Analytics
Authors: Carlos C. Rojas
Robert M. Patton
Barbara G. Beckerman
Publication date: 01-10-2011
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 5/2011
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-011-9685-2

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Springer Medicine

Characterizing Mammography Reports for Health Analytics

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

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