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A hybrid algorithm for clinical decision support in precision medicine based on machine learning.

Zicheng Zhang1, Xinyue Lin1, Shanshan Wu2

  • 1Nanjing University, Nanjing, China.

BMC Bioinformatics
|January 3, 2023
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Summary

This study introduces a hybrid algorithm for biomedical literature retrieval, improving search accuracy for precision medicine. The novel approach enhances PubMed searches, yielding more relevant results for specific disease queries.

Keywords:
Abstract extractionBM25BioBertInformation retrievalMachine learning

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing
  • Machine Learning

Background:

  • Effective retrieval of specific biomedical articles is crucial for advancing precision medicine.
  • Existing search methods often struggle to identify highly relevant documents from large databases like PubMed.

Purpose of the Study:

  • To propose a hybrid algorithm combining BM25, k-means clustering, and BioBert for enhanced biomedical literature retrieval.
  • To improve the identification of articles with similar information regarding specific disease queries in PubMed.

Main Methods:

  • A two-stage retrieval process was developed, starting with an improved BM25 algorithm for initial scoring.
  • K-means clustering was used for abstract extraction, followed by BioBert for document similarity matching.

Main Results:

  • The algorithm was validated using TREC 2017-2019 datasets.
  • The proposed hybrid method demonstrated a 3% increase in P@10 compared to state-of-the-art methods in TREC 2019.

Conclusions:

  • The integrated hybrid approach is effective for specialized medical literature retrieval.
  • The algorithm shows generalizability and potential for clinical decision support in precision medicine.