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J Med Syst. 2011 Oct;35(5):1197-210. doi: 10.1007/s10916-011-9685-2. Epub 2011 Jun 14.

Characterizing mammography reports for health analytics.

Author information

1
Oak Ridge National Lab, Oak Ridge, TN 37831-6085, USA. rojascc@ornl.gov

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.

PMID:
21671070
DOI:
10.1007/s10916-011-9685-2
[Indexed for MEDLINE]

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