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Detecting Redundant Health Survey Questions by Using Language-Agnostic Bidirectional Encoder Representations From

Sunghoon Kang1, Hyewon Park1, Ricky Taira2

  • 1College of Nursing, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 027408483.

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Summary
This summary is machine-generated.

This study developed the SBERT-LaBSE algorithm to measure semantic similarity in health survey questions across languages. It effectively standardizes person-generated health data (PGHD), improving interoperability and research potential.

Keywords:
BERTLaBSEPGHDSBERTbidirectional encoder representations from transformersinteroperabilitylanguage-agnostic BERT sentence embeddingperson-generated health datasemantic similaritysentence-bidirectional encoder representations from transformers

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Area of Science:

  • Health Informatics
  • Natural Language Processing
  • Data Standardization

Background:

  • Person-generated health data (PGHD) is increasingly vital in healthcare and research.
  • Standardization of survey-based PGHD is crucial for usability and interoperability.
  • Existing methods like PROMIS and NIH CDE are valuable, but manual annotation for semantic similarity is labor-intensive and difficult to scale, especially across languages.

Purpose of the Study:

  • To compute semantic similarity among health survey questions in English and Korean.
  • To facilitate the standardization of survey-based PGHD.
  • To develop and evaluate algorithms for cross-lingual semantic similarity assessment.

Main Methods:

  • Compiled multilingual health survey questions from various sources (NIH CDE, PROMIS, Korean agencies, publications).
  • Created a dataset of 1758 question pairs with human-assigned similarity scores.
  • Trained and evaluated four classifiers: bag-of-words, SBERT-BERT, SBERT-LaBSE, and GPT-4o.

Main Results:

  • SBERT-LaBSE achieved superior performance (>0.99 AUC ROC and PR curves) in assessing cross-lingual question similarity.
  • SBERT-LaBSE effectively identified semantic equivalencies between English and Korean questions.
  • While excelling in alignment, SBERT-LaBSE faced challenges with subtle nuances and computational efficiency.

Conclusions:

  • The SBERT-LaBSE algorithm offers a powerful tool for calculating cross-lingual semantic similarity in health surveys.
  • It demonstrates significant advantages over traditional methods and other advanced models for standardizing PGHD.
  • Future work should involve larger multilingual datasets and score normalization for enhanced consistency across health lifelog domains.