Open AccessPredicting the Linguistic Accessibility of Chinese Health Translations: Machine Learning Algorithm Development
Author(s) -
Meng Ji,
Pierrette Bouillon
Publication year - 2021
Publication title -
jmir medical informatics
Language(s) - English
Resource type - Journals
ISSN - 2291-9694
DOI - 10.2196/30588
Subject(s) - random forest , gradient boosting , readability , machine learning , artificial intelligence , decision tree , logistic regression , pairwise comparison , machine translation , computer science , natural language processing , medicine , algorithm , programming language
Background Linguistic accessibility has an important impact on the reception and utilization of translated health resources among multicultural and multilingual populations. Linguistic understandability of health translation has been understudied. Objective Our study aimed to develop novel machine learning models for the study of the linguistic accessibility of health translations comparing Chinese translations of the World Health Organization health materials with original Chinese health resources developed by the Chinese health authorities. Methods Using natural language processing tools for the assessment of the readability of Chinese materials, we explored and compared the readability of Chinese health translations from the World Health Organization with original Chinese materials from the China Center for Disease Control and Prevention. Results A pairwise adjusted t test showed that the following 3 new machine learning models achieved statistically significant improvement over the baseline logistic regression in terms of area under the curve: C5.0 decision tree (95% CI –0.249 to –0.152; P <0.001), random forest (95% CI 0.139-0.239; P <0.001) and extreme gradient boosting tree (95% CI 0.099-0.193; P <0.001). There was, however, no significant difference between C5.0 decision tree and random forest ( P =0.513). The extreme gradient boosting tree was the best model, achieving statistically significant improvement over the C5.0 model ( P =0.003) and the random forest model ( P =0.006) at an adjusted Bonferroni P value at 0.008. Conclusions The development of machine learning algorithms significantly improved the accuracy and reliability of current approaches to the evaluation of the linguistic accessibility of Chinese health information, especially Chinese health translations in relation to original health resources. Although the new algorithms developed were based on Chinese health resources, they can be adapted for other languages to advance current research in accessible health translation, communication, and promotion.