Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong.
School of Languages and Cultures, The University of Sydney, Darlington, Australia.
Comput Intell Neurosci. 2021 Oct 28;2021:1011197. doi: 10.1155/2021/1011197. eCollection 2021.
Neural machine translation technologies are having increasing applications in clinical and healthcare settings. In multicultural countries, automatic translation tools provide critical support to medical and health professionals in their interaction and exchange of health messages with migrant patients with limited or non-English proficiency. While research has mainly explored the usability and limitations of state-of-the-art machine translation tools in the detection and diagnosis of physical diseases and conditions, there is a persistent lack of evidence-based studies on the applicability of machine translation tools in the delivery of mental healthcare services for vulnerable populations. Our study developed Bayesian machine learning algorithms using relevance vector machine to support frontline health workers and medical professionals to make better informed decisions between risks and convenience of using online translation tools when delivering mental healthcare services to Spanish-speaking minority populations living in English-speaking countries. Major strengths of the machine learning classifier that we developed include scalability, interpretability, and adaptability of the classifier for diverse mental healthcare settings. In this paper, we report on the process of the Bayesian machine learning classifier development through automatic feature optimisation and the interpretation of the classifier-enabled assessment of the suitability of original English mental health information for automatic online translation. We elaborate on the interpretation of the assessment results in clinical settings using statistical tools such as positive likelihood ratios and negative likelihood ratios.
神经机器翻译技术在临床和医疗保健领域的应用越来越多。在多文化国家，自动翻译工具为医疗和健康专业人员与英语水平有限或非英语的移民患者进行互动和交流健康信息提供了关键支持。虽然研究主要探索了最先进的机器翻译工具在检测和诊断身体疾病和状况方面的可用性和局限性，但在机器翻译工具在为弱势群体提供精神保健服务方面的适用性方面，仍然缺乏基于证据的研究。我们的研究使用相关向量机开发了贝叶斯机器学习算法，以支持一线卫生工作者和医疗专业人员在向讲西班牙语的少数民族提供精神保健服务时，在使用在线翻译工具的风险和便利性之间做出更明智的决策。我们开发的机器学习分类器的主要优势包括可扩展性、可解释性和对不同精神保健环境的分类器的适应性。在本文中，我们报告了通过自动特征优化和分类器启用的原始英语心理健康信息的自动在线翻译适用性评估的分类器的解释来开发贝叶斯机器学习分类器的过程。我们使用阳性似然比和阴性似然比等统计工具详细阐述了在临床环境中对评估结果的解释。
