Abstract
Evidence-based medicine (EBM) is an inevitable trend in the development of medicine. It effectively improves treatment effect of diseases through combing clinical experience, medical acknowledge, and individualized biological information of patients. The biomedical literature, as the important medical acknowledge source of EBM, could help to discover comorbidity or disease progression patterns. However, due to the strong professionalism of biomedical literature, compared with the general language, the extracted medical phrases have semantic ambiguity problems. Therefore, we propose the high quality medical phrase mining approach (HQMP) for reducing the overdependence on frequency of multiple phrase evaluation and eliminating the semantic ambiguity of bilateral expansion of phrase boundaries. We use the proposed approach to analyze the pathogeny, diagnoses, and treatments of ophthalmopathy with central retinal vein occlusion (CRVO) and glaucoma, and demonstrate the diagnostic frequent disease co-occurrence and sequence patterns mined from medical literatures, to improve the credibility of evidence-based medicine for prevention and treatment of diseases. The experimental results show that HQMP not only improves the quality of medical phrases effectively, but also has fast performance.
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This work was supported by National Natural Science Foundation of China (No. 61701104).
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Wang, L., Gao, X., Zhou, T.H., Liu, W.Q., Sun, C.H. (2020). Mining High Quality Medical Phrase from Biomedical Literatures Over Academic Search Engine. In: Pan, JS., Li, J., Tsai, PW., Jain, L. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 156. Springer, Singapore. https://doi.org/10.1007/978-981-13-9714-1_31
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DOI: https://doi.org/10.1007/978-981-13-9714-1_31
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