Use of a support vector machine for categorizing free-text notes
The use of Natural language processing (NLP) to extract discrete data from free-text documentation in an Electronic Health Record (EHR) is one of the most challenging and rewarding fields of study in the healthcare informatics community. In an article entitled "Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions" published in the Journal of the American Medical Informatics Association (JAMIA) Sep-Oct 2013, the authors describe the use of NLP to identify EHR Progress Notes which pertain to diabetes.[1]
Contents
Abstract
Background
Electronic health record (EHR) users must regularly review large amounts of data in order to make informed clinical decisions, and such review is time-consuming and often overwhelming. Technologies like automated summarization tools, EHR search engines and natural language processing have been shown to help clinicians manage this information.
Objective
To develop a support vector machine (SVM)-based system for identifying EHR progress notes pertaining to diabetes, and to validate it at two institutions.
Materials and Methods
We retrieved 2000 EHR progress notes from patients with diabetes at the Brigham and Women's Hospital (1000 for training and 1000 for testing) and another 1000 notes from the University of Texas Physicians (for validation). We manually annotated all notes and trained a SVM using a bag of words approach. We then used the SVM on the testing and validation sets and evaluated its performance with the area under the curve (AUC) and F statistics.
Results
The model accurately identified diabetes-related notes in both the Brigham and Women's Hospital testing set (AUC=0.956, F=0.934) and the external University of Texas Faculty Physicians validation set (AUC=0.947, F=0.935).
Discussion
Overall, the model we developed was quite accurate. Furthermore, it generalized, without loss of accuracy, to another institution with a different EHR and a distinct patient and provider population.
Conclusion
It is possible to use a SVM-based classifier to identify EHR progress notes pertaining to diabetes, and the model generalizes well.
Comments
This is a placeholder
References
- ↑ Wright A, McCoy AB, Henkin S, Kale A, Sittig DF. Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions. Journal of the American Medical Informatics Association : JAMIA. 2013;20(5):887-890. doi:10.1136/amiajnl-2012-001576.
