INTRODUCTION: Large structured databases of pathology findings are valuable in deriving new clinical insights. However, they are labor intensive to create and generally require manual annotation. There has been some work in the bioinformatics communi...
PURPOSE: Evidence suggests anti-estrogen endocrine therapy (ET) is associated with adverse cognitive effects; however, findings are based on small samples and vary in the cognitive abilities affected. We conducted a meta-analysis to quantitatively sy...
PURPOSE: Extracting information from electronic medical record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine le...
PURPOSE: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.