Abstract
Clinical Texts Are Inherently Complex Due to the Medical Domain Expertise Required for Content Comprehension. in Addition, the Unstructured Nature of These Narratives Poses a Challenge for Automatically Extracting Information. in Natural Language Processing, the Use of Word Embeddings Are an Effective Approach to Generate Word Representations (Vectors) in a Low Dimensional Space. in This Paper We Use a Log-Linear Model (A Type of Neural Language Model) and Linear Discriminant Analysis with a Kernel-Based Extreme Learning Machine (Elm) to Map the Clinical Texts to the Medical Code. Experimental Results on Clinical Texts Indicate Improvement with Elm in Comparison to Svm and Neural Network Approaches.
Recommended Citation
P. Lauren et al., "Clinical Narrative Classification using Discriminant Word Embeddings with Elm," Proceedings of the International Joint Conference on Neural Networks, pp. 2931 - 2938, article no. 7727570, Institute of Electrical and Electronics Engineers, Oct 2016.
The definitive version is available at https://doi.org/10.1109/IJCNN.2016.7727570
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-150900619-9
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
31 Oct 2016
