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

TL;DR

This paper introduces Relevant Word Order Vectorization (RWOV), a novel text structuring algorithm tailored for electronic health records, improving classification accuracy by considering key word repetition and order in unstructured medical texts.

Contribution

The paper presents RWOV, a new vectorization method specifically designed for EHR free-text, outperforming existing approaches like ngrams and word2vec in classifying breast cancer hormone receptor status.

Findings

RWOV achieved higher F1 scores and AUC than baseline methods.

Considering key word order improves classification performance.

RWOV shows promise for structuring unstructured healthcare text.

Abstract

Objective: Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the relevant information is stored in an unstructured format that makes it difficult to use. Natural language processing approaches that attempt to automatically classify the data depend on vectorization algorithms that impose structure on the text, but these algorithms were not designed for the unique characteristics of EHR. Here, we propose a new algorithm for structuring so-called free-text that may help researchers make better use of EHR. We call this method Relevant Word Order Vectorization (RWOV). Materials and Methods: 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 during a recent year, from the unstructured text of pathology reports. Our approach attempts to account for the semi-structured way that healthcare providers often enter information. We compared this approach to the ngrams and word2vec methods. Results: Our approach resulted in the most consistently high accuracy, as measured by F1 score and area under the receiver operating characteristic curve (AUC). Discussion: Our results suggest that methods of structuring free text that take into account its context may show better performance, and that our approach is promising. Conclusion: By using a method that accounts for the fact that healthcare providers tend to use certain key words repetitively and that the order of these key words is important, we showed improved performance over methods that do not.