Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6920633 | Computers in Biology and Medicine | 2018 | 38 Pages |
Abstract
We use a dataset of 150 clinical narratives, 80% of which are used to train our prediction classifier support vector machine, with the remaining 20% used for testing. Semantic extraction and sentiment analysis results yielded precisions of 81% and 70%, respectively. Using a support vector machine, prediction of patients with VTE yielded precision and recall values of 54.5% and 85.7%, respectively.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science Applications
Authors
Susan Sabra, Khalid Mahmood Malik, Mazen Alobaidi,