Cargando…
Predicting Risks of Machine Translations of Public Health Resources by Developing Interpretable Machine Learning Classifiers
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 s...
Autores principales: | Xie, Wenxiu, Ji, Meng, Huang, Riliu, Hao, Tianyong, Chow, Chi-Yin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8391184/ https://www.ncbi.nlm.nih.gov/pubmed/34444538 http://dx.doi.org/10.3390/ijerph18168789 |
Ejemplares similares
-
Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers
por: Xie, Wenxiu, et al.
Publicado: (2021) -
Forecasting Erroneous Neural Machine Translation of Disease Symptoms: Development of Bayesian Probabilistic Classifiers for Cross-Lingual Health Translation
por: Ji, Meng, et al.
Publicado: (2021) -
Forecasting the Suitability of Online Mental Health Information for Effective Self-Care Developing Machine Learning Classifiers Using Natural Language Features
por: Ji, Meng, et al.
Publicado: (2021) -
Probabilistic Prediction of Nonadherence to Psychiatric Disorder Medication from Mental Health Forum Data: Developing and Validating Bayesian Machine Learning Classifiers
por: Ji, Meng, et al.
Publicado: (2022) -
Developing Machine Learning and Statistical Tools to Evaluate the Accessibility of Public Health Advice on Infectious Diseases among Vulnerable People
por: Xie, Wenxiu, et al.
Publicado: (2021)