Artificial Intelligence Software Speeds Up Breast Cancer Risk Prediction, Resolves False-Positives

Artificial Intelligence Software Speeds Up Breast Cancer Risk Prediction, Resolves False-Positives
Artificial intelligence (AI) software developed by a team of researchers at Houston Methodist Hospital is designed to reliably interpret mammogram data and enable doctors to quickly and accurately assess the likelihood of breast cancer risk. The Houston Methodist investigators reported their progress in a new study published online before print in the journal Cancer, explaining that the computer software intuitively translates patient charts into diagnostic information 30 times faster than would be required by a typical human analyst, and with 99 percent accuracy. The original article, "Correlating mammographic and pathologic findings in clinical decision support using natural language processing and data mining methods," observes that a key challenge confronting researchers mining electronic health records such as mammography data is a preponderance of unstructured narrative text which significantly limits usable output. The authors note that breast cancer subtype imaging characteristics have been described previously, but without any standardization of parameters for data mining. In their investigation, the authors searched Houston Methodist Hospital's enterprise-wide data warehouse -- the Methodist Environment for Translational Enhancement and Outcomes Research (METEOR) -- for case histories of patients with Breast Imaging Reporting and Data System, or BI-RADS, (a standard method of reporting mammogram results) category 5 mammogram readings between January 2006 and May 2015 and available pathology reports. They developed natural language processing (NLP) software algorithms that can automatically extract findings from free text mammogram and pathology reports. Then they analyzed correlations between mammography imaging features and breast cancer
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