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.
Authors:
Ryan Cobb, Sahil Puri, Daisy Zhe Wang, Tezcan Baslanti, Azra Bihorac
Bibtex:
@article{,
author = "Ryan Cobb, Sahil Puri, Daisy Zhe Wang, Tezcan Baslanti, Azra Bihorac",
title = "Knowledge Extraction and Outcome Prediction using Medical Notes",
journal = " ICML workshop on Role of Machine Learning in Transforming Healthcare",
year = "2013",
month = "June"
}
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