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Related Experiment Video

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evolutionary Algorithm based Ensemble Extractive Summarization for Developing Smart Medical System.

Chirantana Mallick1, Asit Kumar Das2, Janmenjoy Nayak3

  • 1Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, Howrah, 711103, India.

Interdisciplinary Sciences, Computational Life Sciences
|February 12, 2021
PubMed
Summary

This study introduces an evolutionary algorithm-based ensemble extractive summarization technique for smart medical systems. The novel approach effectively summarizes biomedical literature, outperforming existing methods.

Keywords:
Bio-Medical informaticsClustering coefficientEnsemble summaryMulti-objective evolutionary algorithmSparsity indexSupervised extractive summarization

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Artificial Intelligence

Background:

  • The exponential growth of biomedical literature poses challenges for developing smart medical systems.
  • Effective information retrieval and understanding from medical documents are crucial.
  • Automated summarization techniques are vital for managing this information overload.

Purpose of the Study:

  • To devise an evolutionary algorithm-based ensemble extractive summarization technique for smart medical applications.
  • To leverage hybrid artificial intelligence and natural language processing for biomedical text summarization.
  • To improve the efficiency of searching and understanding relevant information in medical documents.

Main Methods:

  • Utilized abstracts of target and cited articles as base summaries.
  • Applied a multi-objective evolutionary algorithm with concept vectors from Unified Medical Language System (UMLS) terms.
  • Defined fitness functions using graph theory concepts: clustering coefficient and sparsity index.
  • Calculated semantic similarity between sentences and the ensemble summary to identify key sentences.

Main Results:

  • The proposed method was applied to articles from the PubMed MEDLINE database.
  • Experimental results demonstrated that the method competes with and often outperforms state-of-the-art summarization techniques.
  • Statistical tests confirmed the significance and effectiveness of the proposed methodology.

Conclusions:

  • The developed evolutionary algorithm-based ensemble extractive summarization is an effective smart medical application.
  • The hybrid approach combining AI and NLP shows promise for biomedical literature summarization.
  • The technique offers a statistically significant improvement in summarizing complex medical information.