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Background: Standardized coding of plays an important role in radiology reports' secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research.Methods: We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese-English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms.Results: The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup.Conclusions: The paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding. [ABSTRACT FROM AUTHOR]

Background: It has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition.Methods: This paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently.Results: Around 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research.Conclusions: This research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous maintenance of SCCSE, the CTCFs could be precisely retrieved from free text, and the CTCFs arranged in semantic hierarchy of SNOMED CT could greatly improve the meaningful use of electronic health record in China. The methodology is also useful for clinical synonyms enrichment in other languages. [ABSTRACT FROM AUTHOR]