A hybrid medical text classification framework: Integrating attentive rule construction and neural network

Xiang LI, Menglin Cui, Jingpeng Li, Ruibin Bai, Zheng Lu, Uwe Aickelin

Research output: Journal PublicationArticlepeer-review

10 Citations (Scopus)
5 Downloads (Pure)

Abstract

The main objective of this work is to improve the quality and transparency of the medical text classification solutions. Conventional text classification methods provide users with only a restricted mechanism (based on frequency) for selecting features. In this paper, a three-stage hybrid method combining the gated attention-based bi-directional Long Short-Term Memory (ABLSTM) and the regular expression based classifier is proposed for medical text classification tasks. The bi-directional Long Short-Term Memory (LSTM) architecture with an attention layer allows the network to weigh words according to their perceived importance and focus on crucial parts of a sentence. Feature words (or keywords) extracted by ABLSTM model are utilized to guide the regular expression rule construction. Our proposed approach leverages the advantages of both the interpretability of rule-based algorithms and the computational power of deep learning approaches for a production-ready scenario. Experimental results on real-world medical online query data clearly validate the superiority of our system in selecting domain-specific and topic-related features. Results show that the proposed approach achieves an accuracy of 0.89 and an F1-score of 0.92 respectively. Furthermore, our experimentation also illustrates the versatility of regular expressions as a user-level tool for focusing on desired patterns and providing interpretable solutions for human modification.

Original languageEnglish
Pages (from-to)345-355
Number of pages11
JournalNeurocomputing
Volume443
DOIs
Publication statusPublished - 5 Jul 2021

Keywords

  • Attention mechanism
  • Deep learning
  • Hybrid system
  • Text classification

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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