Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records

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ID: 85182
2019
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Abstract
Abstract Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR. Here, we propose Relevant Word Order Vectorization (RWOV) to aid with structuring. RWOV is based on finding the positional relationship between the most relevant words to predicting the class of a text. This facilitates machine learning algorithms to use the interaction of not just keywords but positional dependencies (e.g. a relevant word occurs 5 relevant words before some term of interest). As a proof-of-concept, we attempted to classify the hormone receptor status of breast cancer patients treated at the University of Kansas Medical Center, comparing RWOV to other methods using the F1 score and AUC. RWOV performed as well as, or better than other methods in all but one case. For F1 score, RWOV had a clear edge on most tasks. AUC tended to be closer, but for HER2, RWOV was significantly better for most comparisons. These results suggest RWOV should be further developed for EHR-related NLP.
Reference Key
thompson2019relevantscientific Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Thompson, Jeffrey;Hu, Jinxiang;Mudaranthakam, Dinesh Pal;Streeter, David;Neums, Lisa;Park, Michele;Koestler, Devin C.;Gajewski, Byron;Jensen, Roy;Mayo, Matthew S.;
Journal Scientific reports
Year 2019
DOI
DOI not found
URL
http://link.springer.com/article/10.1038/s41598-019-45705-y
Keywords
Medicine Science neurosciences. biological psychiatry. neuropsychiatry neurology. diseases of the nervous system

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