Knowledge Extraction and Outcome Prediction using Medical Notes
Ryan Cobb, Sahil Puri, Daisy Wang, Tezcan Ozrazgat Baslanti, Azra Bihorac · 2013
The increasing use of electronic health records (EHR) has allowed for an unprecedented ability to perform analysis on patient data. By training a number of statistical machine learning classifiers over the unstructured text found in admission notes and operating procedures, prediction of a surgical procedure’s outcome can be performed. We extend an initial bag-of-words model to a bag-of-concepts model, which uses cTakes and UMLS to extract medical terms and concepts from medical notes. We also extend cTakes to improve the knowledge extraction. Lastly, we propose a knowledge exchange component, which allows physicians to provide feedback on outcome results to further tune the underlying classifier.