Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
Bibliographic Data
| ID | 15514355 |
|---|---|
| Authors | Wenxiu Xie (0000-0002-8528-5193, City University of Hong Kong), Meng Ji (0000-0002-7463-9208, The University of Sydney, corresponding author), Riliu Huang (The University of Sydney), Tianyong Hao (0000-0002-9792-3949, South China Normal University), Chi-Yin Chow (0000-0002-9566-0743, City University of Hong Kong) |
| Year | 2021 |
| Volume | 18 |
| Issue | 16 |
| Pages | 8789-8789 |
| Publication date | 2021-08-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph18168789 |
| PMID | 34444538 |
| OpenAlex | W3195413266 |
| Language | EN |
| Citations received | 2 |
| References cited | 15 |
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 p p p p = 0.0002, SE = 0.026) and the automatically optimised classifier (22) (M = 0.633, SD = 0.141, p = 0.0133, SE = 0.026)
Classifier (UML · Machine learning · Naive Bayes classifier · Pattern recognition (psychology · Probabilistic classification · Random forest · Support vector machine · Biomedical Text Mining and Ontologies · Computer Science · Natural Language Processing Techniques · Topic Modeling · Artificial Intelligence
| Citations per year | 0.5 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |