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Predicting Health Material Accessibility: Development of Machine Learning Algorithms.

Meng Ji1, Yanmeng Liu1, Tianyong Hao2

  • 1School of Languages and Cultures, The University of Sydney, Sydney, Australia.

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Summary
This summary is machine-generated.

This study reveals that semantic features, not just medical jargon, impact health text comprehension for non-native English speakers. Machine learning models accurately predict health information accessibility for diverse audiences.

Keywords:
Chinese languageaccessibilityalgorithmcognitioncognitive accessibilityhealth educationhealth education materialshealth informationhealth textmachine learningpredictionreadabilitysemanticsemantic features

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Area of Science:

  • Natural Language Processing in Health Communication
  • Machine Learning for Readability Assessment
  • Cognitive Science of Health Information Processing

Background:

  • Traditional health information understandability research relies on medical readability formulas, assuming jargon is the primary barrier.
  • This study challenges the assumption that medical domain knowledge and jargon are the sole obstacles to public health information access.
  • Focus is on non-English speaking backgrounds with higher education, where semantic features may be more critical than previously thought.

Purpose of the Study:

  • To explore multidimensional semantic features for developing machine learning algorithms to predict cognitive accessibility of English health materials.
  • To compare various machine learning algorithms for their effectiveness in evaluating health information accessibility for non-native English speakers.
  • To assess health information cognitive accessibility for young adults in Australian tertiary institutes with limited exposure to English health education.

Main Methods:

  • Utilized 113 semantic features to measure content complexity and accessibility of 1000 English health texts.
  • Collected data from Australian and international health organization websites, rated by overseas tertiary students.
  • Compared machine learning models (decision tree, SVM, ensemble tree, logistic regression) using 10-fold cross-validation and hyperparameter optimization.

Main Results:

  • Ensemble tree (LogitBoost) demonstrated superior performance with an AUC of 0.97, sensitivity of 0.966, specificity of 0.972, and accuracy of 0.969.
  • Decision tree and SVM models also showed strong performance, outperforming logistic regression.
  • Ensemble tree significantly improved upon SVM and decision tree in key performance metrics.

Conclusions:

  • Cognitive accessibility of English health texts extends beyond word and sentence length, challenging traditional readability formulas.
  • Machine learning models based on semantic features effectively predict health resource accessibility for non-native English speakers.
  • Semantic features, including cognitive ability, communication, power dynamics, and lexical familiarity, significantly contribute to health information comprehension.