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State-of-the-Art Evidence Retriever for Precision Medicine: Algorithm Development and Validation.

Qiao Jin1, Chuanqi Tan1, Mosha Chen1

  • 1Alibaba Group, Hangzhou, China.

JMIR Medical Informatics
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

Physicians can now find crucial evidence for precision medicine (PM) therapies more easily with the new PM-Search system. This tool prioritizes evidence quality, improving access to vital clinical information for personalized cancer care.

Keywords:
active learningalgorithm developmentbig datadata retrievaldigital health interventionevidence-based medicineinformation retrievalprecision medicinepretrained language models

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Oncology

Background:

  • Precision medicine (PM) enables personalized therapies based on patient-specific clinical and genetic data.
  • The rapid growth of scientific literature makes it challenging for clinicians to find relevant evidence for personalized treatments.
  • Accessing comprehensive clinical evidence is crucial for effective evidence-based precision medicine.

Purpose of the Study:

  • To develop and evaluate the PM-Search system for retrieving clinical literature relevant to precision medicine therapies.
  • To facilitate the identification of evidence supporting or refuting specific cancer therapy recommendations.
  • To improve the efficiency and accuracy of evidence retrieval for practicing evidence-based precision medicine.

Main Methods:

  • The PM-Search system integrates a large-scale baseline retriever using query expansion and keyword matching (ElasticSearch).
  • An evidence reranker employs pretrained language models fine-tuned on expert annotations derived from active learning.
  • The system prioritizes document candidates based on the quality of clinical evidence presented.

Main Results:

  • PM-Search demonstrated superior performance in retrieving high-quality clinical evidence at the Text Retrieval Conference PM Track 2020.
  • Achieved top scores in normalized discounted cumulative gain (0.4780) and exponential normalized discounted cumulative gain (0.4519) at rank 30.
  • Significantly outperformed competing systems in identifying relevant and high-quality evidence for precision medicine.

Conclusions:

  • PM-Search is a state-of-the-art search engine designed to support evidence-based precision medicine.
  • A novel active learning strategy using Bidirectional Encoder Representations from Transformers for Biomedical Text Mining models evidence quality.
  • Evidence quality is a critical factor distinct from general relevance, necessitating specialized modeling for effective PM search.