Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Computer-aided diagnosis of breast lesions in medical images
Computing in Science and Engineering
Efficient Retrieval of Similar Time Sequences Under Time Warping
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
TF-ICF: A New Term Weighting Scheme for Clustering Dynamic Data Streams
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Temporal Analysis of Mammograms Based on Graph Matching
IWDM '08 Proceedings of the 9th international workshop on Digital Mammography
A genetic algorithm for learning significant phrase patterns in radiology reports
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Computer-aided detection and diagnosis of breast cancer with mammography: recent advances
IEEE Transactions on Information Technology in Biomedicine
Information Extraction for Clinical Data Mining: A Mammography Case Study
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Temporal pattern discovery in longitudinal electronic patient records
Data Mining and Knowledge Discovery
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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.