Discriminant Document Embeddings with an Extreme Learning Machine for Classifying Clinical Narratives

Abstract

The Unstructured Nature of Clinical Narratives Makes Them Complex for Automatically Extracting Information. Feature Learning is an Important Precursor to Document Classification, a Sub-Discipline of Natural Language Processing (Nlp). in Nlp, Word and Document Embeddings Are an Effective Approach for Generating Word and Document Representations (Vectors) in a Low-Dimensional Space. This Paper Uses Skip-Gram and Paragraph Vectors-Distributed Bag of Words (Pv-Dbow) with Multiple Discriminant Analysis (Mda) to Arrive at Discriminant Document Embeddings. a Kernel-Based Extreme Learning Machine (Elm) is Used to Map the Clinical Texts to the Medical Code. Experimental Results on Clinical Texts Indicate overall Improvement Especially for the Minority Classes.

Department(s)

Engineering Management and Systems Engineering

Comments

National Science Foundation, Grant NSF-1614024

Keywords and Phrases

Clinical narratives; Document classification; Document embeddings; Extreme learning machines; Feature learning; Multiple discriminant analysis; PV-DBOW; Skip-gram; Word embeddings

International Standard Serial Number (ISSN)

1872-8286; 0925-2312

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Elsevier, All rights reserved.

Publication Date

14 Feb 2018

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