Alison Callahan
Research Scientist
Shah Lab, Stanford University

Abstract:
Osteoarthritis is the most common cause of adult disability in the United States, and more than 1 million joint replacements are carried out each year to manage this disease. A significant proportion of patients who undergo joint replacement surgery do not experience an improvement in pain, and some go on to have significant complications requiring joint implant revision surgery. To better understand outcomes following joint replacement, we aim to quantify patient-reported pain before and after surgery, and to combine this information with structured data from electronic health records. We accomplish this by extracting information from clinical notes, which describe patient experience and clinician practice in ways not captured by billing codes, lab reports and medication orders. Natural variation in clinical language and reporting styles, and the cost of creating labeled datasets for supervised machine learning approaches, pose unique challenges for extracting information from unstructured clinical text. I will present in-progress work to overcome these challenges using data programming and text mining to extract mentions of patient-reported pain and implant details from the clinical notes of joint replacement patients.