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Characterizing Mammography Reports for Health Analytics

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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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Notes

  1. http://apps.who.int/classifications/apps/icd/icd10online/index.htm?navi.htm+ka00

  2. Breast Imaging Reporting and Data System, developed by the American College of Radiology.

  3. This, of course, does not hold for every document and every human (within a given language) since specialized terminology is not universally accessible. It is, however, a reasonable assumption within a field, e.g., health sciences.

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Acknowledgements

We thank Robert M. Nishikawa, Ph.D., Department of Radiology, University of Chicago, for providing the large dataset of unstructured mammography reports.

Prepared by Oak Ridge National Laboratory, P. O. Box 2008, Oak Ridge, Tennessee, 37831-6285, managed by UT-Battelle, LLC, for the U.S. Department of Energy Under contract DE-AC05-00OR22725. Research partially sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, LDRD #5327.

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the U.S. Department of Energy.

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  1. Oak Ridge National Lab, One Bethel Valley Road, P.O. Box 2008, MS-6085, Oak Ridge, TN, 37831-6085, USA

    Carlos C. Rojas, Robert M. Patton & Barbara G. Beckerman

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  1. Carlos C. Rojas
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  2. Robert M. Patton
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Correspondence to Carlos C. Rojas.

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Rojas, C.C., Patton, R.M. & Beckerman, B.G. Characterizing Mammography Reports for Health Analytics. J Med Syst 35, 1197–1210 (2011). https://doi.org/10.1007/s10916-011-9685-2

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