A Report On E-C Translation Of Machine Learning And AI For Healthcare:Big Data For Improved Health Outcome(Chapter 4)

As a result of rapid development of science and technology,Artificial Intelligence(AI)has found an increasingly wide utilization in medical field.This report is based on the translation practice of Machine Learning and AI in Healthcare: Big Data for Improved Health Outcome(Chapter 4).The selected text introduces the key concepts of medical machine learning systems and the application of related algorithms in healthcare,the translation of which is of great importance for the development of big data in healthcare in China.Through text analysis,it is found that there are a number of terms in the source text,and the language is presented objectively and logically,which are in line with the typical features of English of Science and Technology(EST)texts.Guided by Sperber and Wilson’s Relevance Theory,some translation cases at the lexical,syntactic and textual levels are analyzed,focusing on the principle of optimal relevance.It is exhibited in the report how translation skills can be used to achieve optimal relevance between the source text author’s intentions and the expectations of the target readers through an ostensiveinferential process: at the lexical level,literal translation,zero-translation,annotation and meaning extension are used;change of voice and restructuring are applied at the syntactic level;and sematic repetition and combination are appropriate for the textual level.This translation report provides a different approach to the study of EST translation from the perspective of Relevance Theory,with a basis of seeking optimal relevance for the selection of translation skills.