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This summary is machine-generated.

Patients struggle to understand complex health information online. This study develops an information retrieval system to better match patient queries with expert health documents, improving comprehension.

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Area of Science:

  • Health Informatics
  • Information Retrieval
  • Natural Language Processing

Background:

  • Patients increasingly access online health information and Electronic Health Records (EHRs).
  • A significant barrier to understanding is the language difference between medical experts and laypersons.
  • Existing systems may not effectively bridge this knowledge gap for patient queries.

Purpose of the Study:

  • To investigate the challenge of matching patient queries to expert health documents.
  • To improve patient comprehension of trustworthy online health information.
  • To explore query expansion strategies within the Information Retrieval (IR) field.

Main Methods:

  • Utilized a state-of-the-art Information Retrieval (IR) system, Indri.
  • Employed semantic resources to enhance query understanding.
  • Explored and evaluated different query expansion strategies.

Main Results:

  • Achieved a Precision at 10 (P@10) of up to 0.6740.
  • Achieved a Recall at 10 (R@10) of up to 0.7610.
  • Achieved a Normalized Discounted Cumulative Gain at 10 (NDCG@10) of up to 0.6793.

Conclusions:

  • The developed IR approach shows promise in associating patient queries with relevant expert health documents.
  • Query expansion strategies are effective in improving the retrieval of understandable health information.
  • This work contributes to making complex medical information more accessible to patients.