Improving the Factual Accuracy of Abstractive Clinical Text Summarization using Multi-Objective Optimization

Amanuel Alambo, Tanvi Banerjee, Krishnaprasad Thirunarayan, Mia Cajita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

While there has been recent progress in abstractive summarization as applied to different domains including news articles, scientific articles, and blog posts, the application of these techniques to clinical text summarization has been limited. This is primarily due to the lack of large-scale training data and the messy/unstructured nature of clinical notes as opposed to other domains where massive training data come in structured or semi -structured form. Further, one of the least explored and critical components of clinical text summarization is factual accuracy of clinical summaries. This is specifically crucial in the healthcare domain, cardiology in particular, where an accurate summary generation that preserves the facts in the source notes is critical to the well-being of a patient. In this study, we propose a framework for improving the factual accuracy of abstractive summarization of clinical text using knowledge-guided multi-objective optimization. We propose to jointly optimize three cost functions in our proposed architecture during training: generative loss, entity loss and knowledge loss and evaluate the proposed architecture on 1) clinical notes of patients with heart failure (HF), which we collect for this study; and 2) two benchmark datasets, Indiana University Chest X-ray collection (IU X-Ray), and MIMIC-CXR, that are publicly available. We experiment with three transformer encoder-decoder architectures and demonstrate that optimizing different loss functions leads to improved performance in terms of entity-level factual accuracy.

Original languageAmerican English
Title of host publication2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
PublisherIEEE
Pages1615-1618
Number of pages4
ISBN (Electronic)978-1-7281-2782-8
ISBN (Print)978-1-7281-2783-5
DOIs
StatePublished - Sep 8 2022
Event44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022 - Glasgow, United Kingdom
Duration: Jul 11 2022Jul 15 2022

Conference

Conference44th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2022
Country/TerritoryUnited Kingdom
CityGlasgow
Period7/11/227/15/22

ASJC Scopus Subject Areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Keywords

  • Benchmarking
  • Cardiology
  • Electric Power Supplies
  • Health Facilities
  • Humans
  • Knowledge
  • Clinical Text Summarization
  • Multi-Objective Optimization
  • Transformers
  • Heart Failure
  • Named Entity Recognition
  • Knowledge Bases
  • Factual Accuracy

Disciplines

  • Computer Sciences
  • Engineering

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