ArticleInternational journal of environmental research and public health2021
Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers.
Article in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Evaluating the Multilingual Accessibility of Health Websites for Immigrants and Ethnic Minorities: A Methodological Systematic Review.Journal of immigrant and minority health · 2026Review
- The Use of Machine Translation for Outreach and Health Communication in Epidemiology and Public Health: Scoping Review.JMIR public health and surveillance · 2023Article
- Predicting HIV Status among Men Who Have Sex with Men in Bulawayo & Harare, Zimbabwe Using Bio-Behavioural Data, Recurrent Neural Networks, and Machine Learning Techniques.Tropical medicine and infectious disease · 2022Article
- Analysis of Language Translations of State Governments' Coronavirus Disease 2019 Vaccine Websites.Health equity · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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,
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Registered trials
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