English Chinese Medical Dictionary Free Download

Traditional Chinese Medicine is one of the popularly applied health resources across the globe. Driven by domestic and international demands, WHO is developing benchmarking documents for training and practice of traditional Chinese medicine, and there is an urgent need to develop standard terminologies to support the development and use of these benchmarking documents as well as other traditional Chinese medicine technical materials. By setting related norms and standards, this document helps to address the issues related to terminology on traditional Chinese medicine. It offers an essential tool for traditional Chinese medicine professionals, policy-makers, health workers and the general public to use the same concepts, understanding and definitions in communications, health care services and medical records, as well as in related technical and training resources.

UBC students Meiying Zhuang and Wynn Tran, supervised by Digital Emergency Medicine under the Doctors, Patients and Society (DPAS) course at UBC Faculty of Medicine, have created a new phrasebook to support clinicians for medical encounters between English speaking medical students and Mandarin speaking patients. The phrasebook contains Mandarin Pronunciation and Simplified Chinese.

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At least 24 Chinese-English dictionaries of Chinese Medicine have been published in China during the recent 24 years (1984-2003). This thesis comments on "A Practical Dictionary of Chinese Medicine" by Wiseman, agreeing on its establishing principles, sources and formation methods of the English system of Chinese medical terminology, and pointing out the defect. The author holds that study on the origin and development of TCM terms, standardization of Chinese medical terms in different layers, i.e. Chinese medical in classic, in commonly used modern TCM terms, and integrative medical texts, are prerequisites to the standardization of English translation of Chinese medical terms.

The process of knowledge graph-based medical QA [4] systems can be divided into three steps when using a knowledge graph based medical QA systems, including intent understanding [5], answer retrieval [6] and answer generation [7, 8]. Intent understanding is to analyze what the users want and plays a core role in the whole answer process. It can be treated as a classification problem if we restrict the intended scope in advance. Traditional methods of intent classification include keyword matching [9], template matching [10], etc. The disadvantage of these methods is their poor generalization ability. Recently, more deep-learning-based approaches gradually attract more and more attention and have achieved excellent performance in intention classification.

The evaluation metric is the accuracy p. The reason we focus on accuracy is that the accuracy of the intent classification plays a decisive factor in the subsequent steps of the medical QA task. The more accurate that the intent classification is, the better that the system performs. The accuracy p is defined as follows:

Knowing the appropriate terms and translations for body parts, symptoms, diagnoses, and treatments, as well as general medical language can help improve practitioner-patient encounters by ensuring clarity and effective communication.

advance care directive (or advance medical directive): A legal document that describes the kind of medical care a person want if an accident or illness leaves him or her unable to make or communicate decisions.

control group: A group of people in a medical study who receive either no treatment or the standard treatment, which is compared against a group who receive the treatment being studied.

During the 100 years from 1850 to 1949, six English textbooks on internal medicine were translated into Chinese and published. Publication of these books was a response to the increased demand for Chinese textbooks after the opening of several Western-style hospitals and medical schools in China where the instruction was in Chinese. Throughout this period, textbooks translated from English were regarded as symbols of mainstream and authority within medical communities in China. There was a shift of translators from British and American medical missionaries to Chinese medical elites. Publishers also changed from missionary hospitals or missionary organizations to the Chinese Medical Association, which was led by ethnic Chinese. After the 1950s, translation activity continued in Taiwan, but it was halted in China until after the Cultural Revolution. This paper provides bibliographic information about these books. The transition of medical authority in China during this 100-year period is also reviewed through the successive publication of translated textbooks on internal medicine.

Medical named entity recognition (NER) is an area in which medical named entities are recognized from medical texts, such as diseases, drugs, surgery reports, anatomical parts, and examination documents. Conventional medical NER methods do not make full use of un-labelled medical texts embedded in medical documents. To address this issue, we proposed a medical NER approach based on pre-trained language models and a domain dictionary. First, we constructed a medical entity dictionary by extracting medical entities from labelled medical texts and collecting medical entities from other resources, such as the Yidu-N4K data set. Second, we employed this dictionary to train domain-specific pre-trained language models using un-labelled medical texts. Third, we employed a pseudo labelling mechanism in un-labelled medical texts to automatically annotate texts and create pseudo labels. Fourth, the BiLSTM-CRF sequence tagging model was used to fine-tune the pre-trained language models. Our experiments on the un-labelled medical texts, which were extracted from Chinese electronic medical records, show that the proposed NER approach enables the strict and relaxed F1 scores to be 88.7% and 95.3%, respectively.

Medical records are important resources in which patients' diagnosis and treatment activities in hospitals are documented. In recent years, many medical institutions have done significant work in archiving electronic medical records. Handwritten medical records are gradually being replaced by digital ones. Many researchers strive for extracting medical knowledge from digital data, using medical knowledge to help medical professionals understand potential causes of various symptoms, and building medical decision support systems.

There are medical NER methods and models developed for extracting medical knowledge from data sets. However, these methods and models are supervised methods which help us learn features from labelled data. In these methods, un-labelled data cannot be used effectively and are often discarded. As a result, medical information hidden in un-labelled texts cannot be recognized. To address this issue, we proposed a medical NER approach based on the combination of pre-trained language models and a domain dictionary. We employed the pre-trained language models and the in-domain training technique to extract medical knowledge from un-labelled medical texts.

We constructed a medical domain entity dictionary and integrated it into pre-trained language models so that the recognition rate (measured by F1 score) of named entities has been significantly improved.

We fed un-labelled medical texts to pre-trained language models for in-domain training. Experimental results show that the proposed NER approach with the help of the in-domain training technique can improve the performance of named entity recognition in medical communities.

The rest of this paper is organized as follows: Section 2 describes previous research work relative to medical NER. With a good understanding of the state-of-the-art practice in medical knowledge extraction, we present our medical NER approach in Section 3. Section 4 describes the evaluation of the medical NER approach when this approach is used for recognition of medical knowledge from the CCKS-2020-CEMR data set. The evaluation results of the NER approach with the help of the pre-trained language models and an entity dictionary are also analyzed in this section. The paper is concluded in Section 5 by summarizing the contributions of the research work and outlining the future research directions.

Medical NER [2] is a basic task for extracting knowledge representation from electronic medical records. Solutions to medical NER can be roughly categorized into two categories: traditional machine learning based methods and neural network based methods. The traditional machine learning based methods mainly treat NER as a sequence labelling task. Wang et al. [3] conducted a study by using conditional random fields (CRFs), support vector machines (SVM), and maximum entropy (ME) to recognize symptoms and pathogenesis in Chinese EMRs. Wang et al. [4] investigated the use of CRF in different feature sets and recognized symptom names from clinical notes of traditional Chinese medicine. Xu et al. [5] proposed a joint model that integrated segmentation and NER simultaneously to improve the performance of NER in Chinese discharge summaries. These methods rely heavily on hand-crafted features.

Unlike the conventional machine learning based methods, the neural network based methods have a training process (or an end-to-end process) which automatically learns from data features. There are a group of studies dealing with medical NER using neural network based methods. Among these studies, Wu et al. [6] developed a deep neural network approach for NER in Chinese clinical texts. Yang et al. [7] combined the characteristics of Chinese electronic language structure, developed labelling rules for named entity recognition in Chinese electronic medical records, and completed the natural language processing research in extracting knowledge from Chinese EMRs. Yang et al. [8] used the combination model of Bi-directional Long Short-Term Memory (BiLSTM) and CRF to extract medical entities from admission records and discharge summaries. Additionally, Chowdhury et al. [9] proposed a multitask bi-directional Recurrent Neural Network (RNN) model to recognize diseases, symptoms, and other entities in Chinese electronic medical records. The RNN model employed two different task layers (the word embedding and character embedding layers) to improve the accuracy of extracting entity information from EMRs data set. Furthermore, Wan et al. [10] proposed a Chinese medical NER method based on joint training of Chinese characters and words. This model took Chinese language features into account and added semantic information of words in its NER process. ff782bc1db