Design considerations for a hierarchical semantic compositional framework for medical natural language understanding

Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with th...

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Published inPloS one Vol. 18; no. 3; p. e0282882
Main Authors Taira, Ricky K, Garlid, Anders O, Speier, William
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 16.03.2023
Public Library of Science (PLoS)
Subjects
Architecture
Big Data
Biology and Life Sciences
Cognition
Cognitive ability
Computational linguistics
Computer and Information Sciences
Deep learning
Design
Design services
Engineering and Technology
Health aspects
Health informatics
Humans
Language
Language processing
Management
Memory
National libraries
Natural language
Natural language interfaces
Natural Language Processing
Ontology
Semantics
Social Sciences
Software
Substrates
United States
United Kingdom
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Summary:Medical natural language processing (NLP) systems are a key enabling technology for transforming Big Data from clinical report repositories to information used to support disease models and validate intervention methods. However, current medical NLP systems fall considerably short when faced with the task of logically interpreting clinical text. In this paper, we describe a framework inspired by mechanisms of human cognition in an attempt to jump the NLP performance curve. The design centers on a hierarchical semantic compositional model (HSCM), which provides an internal substrate for guiding the interpretation process. The paper describes insights from four key cognitive aspects: semantic memory, semantic composition, semantic activation, and hierarchical predictive coding. We discuss the design of a generative semantic model and an associated semantic parser used to transform a free-text sentence into a logical representation of its meaning. The paper discusses supportive and antagonistic arguments for the key features of the architecture as a long-term foundational framework.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0282882