Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification.
Journal:
Journal of the American Medical Informatics Association : JAMIA
Published Date:
Nov 1, 2019
Abstract
OBJECTIVE: Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset.