Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Article number8789
Journal / PublicationInternational Journal of Environmental Research and Public Health
Volume18
Issue number16
Online published20 Aug 2021
Publication statusPublished - Aug 2021

Abstract

We aimed to develop machine learning classifiers as a risk-prevention mechanism to help medical professionals with little or no knowledge of the patient’s languages in order to predict the likelihood of clinically significant mistakes or incomprehensible MT outputs based on the features of English source information as input to the MT systems. A MNB classifier was developed to provide intuitive probabilistic predictions of erroneous health translation outputs based on the computational modelling of a small number of optimised features of the original English source texts. The best performing multinominal Naïve Bayes classifier (MNB) using a small number of optimised features (8) achieved statistically higher AUC (M = 0.760, SD = 0.03) than the classifier using high-dimension natural features (135) (M = 0.631, SD = 0.006, p < 0.0001, SE = 0.004) and the automatically optimised classifier (22) (M = 0.7231, SD = 0.0084, p < 0.0001, SE = 0.004). Furthermore, MNB (8) had statistically higher sensitivity (M = 0.885, SD = 0.100) compared with the full-feature classifier (135) (M = 0.577, SD = 0.155, p < 0.0001, SE = 0.005) and the automatically optimised classifier (22) (M = 0.731, SD = 0.139, p < 0.0001, SE = 0.0023). Finally, MNB (8) reached statistically higher specificity (M = 0.667, SD = 0.138) compared to the full-feature classifier (135) (M = 0.567, SD = 0.139, p = 0.0002, SE = 0.026) and the automatically optimised classifier (22) (M = 0.633, SD = 0.141, p = 0.0133, SE = 0.026).

Research Area(s)

  • multinominal naïve bayes classifier, public health education and promotion, machine learning, digital vulnerability

Citation Format(s)

Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers. / Xie, Wenxiu; Ji, Meng; Huang, Riliu; Hao, Tianyong; Chow, Chi-Yin.

In: International Journal of Environmental Research and Public Health, Vol. 18, No. 16, 8789, 08.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review