Information Extraction for Clinical Data Mining: A Mammography Case Study

Abstract—Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Houssam Nassif, Ryan Woods, Elizabeth S. Burnside, Mehmet Ayvaci, Jude W. Shavlik, David Page
• Keywords: Clinical Data Mining, Data Mining, Free Text, ICDM 2009