5. Discussion and perspective
Based on the perspective of translate information, Figure 3 shows the textual textual challenges of text mining, aimed at processing health information collected from different biomedical sources by seizing various NLP technologies, and many clinical translation research applications. Translate information refers to the data science and computational approach application to bridge the interval between biomedical research and clinical practice. In this context, biomedical text mining plays an important role in obtaining important information from large quantities of biomedical texts, such as electronic health notes, scientific literature, clinical trial reports, medical books, clinical skills guidelines, and even the Internet. By using various NLP technologies, including named entity recognition, normalization, characteristics, relevance of relevance, event obtaining, text -text, text -graph, and text generation, biomedical text mining provides the efficient processing and evaluation of biomedical texts, Translation of applications such as medical text and information processing, data management, management standards, health standards, questions-answer, predicting disease, epidemic forecasting, clinical decision, and discovering knowledge.
Those with -set:
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(6) Liang people, Faculty of Business Information, Shanghai Business School, Shanghai, 201400, China;
(7) Zuofeng Li, Takeda Co Ltd., Shanghai, 200040, China;
(8) Buzhou Tang, Department of Computer Science, Harbin Institute of Technology, Shenzhen, 518055, China;
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