Abstract
With the rapid development of intelligent medical, mobile medicine, health management self-diagnosis, big data management and analysis have become hot research areas. In this paper, we proposed a medical text-based processing method (TLC algorithm), which can enhance feature semantic associations without losing useful information, and effectively discovery the potential value knowledge in medical texts. It can adaptively classify the topics of disease based on specific terms, construct disease-department lexicons according to the weighted coefficients. Our proposed algorithm will effectively mine the underlying disease topics in the mass medical community text data, which can discover the patients high concerned diseases and symptoms, provide the reference of pathological symptoms to doctors, and support the decision-making treatment programs.
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Acknowledgment
This work was supported by National Natural Science Foundation of China (No. 61701104), and by the Natural Science Foundation of Jilin Province of China (No. 20190201194JC).
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Zhou, T.H., Liu, W.Q., Wang, L., Li, J. (2020). Popular Disease Topics Mining Method for Online Medical Community. In: Sitek, P., Pietranik, M., Krótkiewicz, M., Srinilta, C. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Communications in Computer and Information Science, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-15-3380-8_11
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DOI: https://doi.org/10.1007/978-981-15-3380-8_11
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