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

TL;DR

This paper proposes a hierarchical semantic compositional framework inspired by human cognition to improve the logical interpretation of clinical text in medical NLP systems, aiming to enhance understanding accuracy.

Contribution

It introduces a novel hierarchical semantic model and parser architecture based on cognitive principles to advance medical NLP interpretative capabilities.

Findings

Framework aligns with cognitive mechanisms like semantic memory and predictive coding.

Semantic parser effectively transforms free-text into logical representations.

Supports the development of more accurate medical NLP systems.

Abstract

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 about 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 including 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.