Wenxiu Xie|Meng Ji|Mengdan Zhao|Tianqi Zhou|Fan Yang|Xiaobo Qian|Chee W. Chow|Kam-Yiu Lam|Tianyong Hao
Detecting Symptom Errors in Neural Machine Translation of Patient Health Information on Depressive Disorders: Developing Interpretable Bayesian Machine Learning Classifiers
City University of Hong Kong|University of Sydney|University of Sydney|University of Sydney|South China Normal University|City University of Hong Kong|City University of Hong Kong|South China Normal University
background due convenience wide availability low usage cost neural machine translation nmt increasing applications diverse clinical settings webbased selfdiagnosis diseases given developing nature nmt tools pose safety risks multicultural communities limited bilingual skills low education low health literacy research needed scrutinise reliability credibility usability automatically translated patient health information objective aimed develop highperforming bayesian machine learning classifiers assist clinical professionals healthcare workers assessing quality usability nmt depressive disorders tool require prior knowledge frontline health medical professionals target language used patients methods used relevance vector machine rvm increase generalisability clinical interpretability classifiers typical sparse bayesian classifier less prone overfitting small training datasets optimised rvm leveraging automatic recursive feature elimination expert feature refinement perspective health linguistics evaluated diagnostic utility bayesian classifier different probability cutoffs terms sensitivity specificity positive negative likelihood ratios clinical thresholds diagnostic tests finally illustrated interpretation rvm tool clinic using bayes nomogram results automatic expertbased feature optimisation bestperforming rvm classifier rvmdufs12 gained highest auc 08872 among 52 competing models distinct optimised normalised features sets also statistically higher sensitivity specificity compared models evaluated diagnostic utility bestperforming model using bayes nomogram positive likelihood ratio lr 462 95 ci 253 843 associated posterior probability odds 83 50 95 ci 73 90 meaning approximately 10 12 english texts positive test likely contain information would cause clinically significant conceptual errors translated google negative likelihood ratio lr 018 95 ci 010035 associated posterior probability odds 16 02 95 ci 10 27 meaning 10 12 english texts negative test safely translated using google