Xuefeng Long
ABSTRACT
In order to solve the problems of low efficiency of key information extraction, single type of data extracted by model, and poor data correlation in multi-source data generated in the process of COVID-19 transmission, this paper proposed a multi-source COVID-19 data extraction and analysis method. Methods The data of epidemic transmission in Yangzhou, Shijiazhuang and Chongqing were taken as examples. The basic information of patient flow was extracted by Chinese natural language processing technology, and the complex activity tracks of patients were extracted based on Bert-BilSTM-CRF model, and then the city-scale COVID-19 risk index was constructed by combining the basic information and behavior tracks of patients. Compared with HMM, CRF, BiLSTM and BILSTM-CRF, Bert-BILSTM-CRF model has the highest accuracy of 96.08%. Pearson correlation coefficient and Cox proportional hazards model analysis showed that the number of people over 59 years old, the number of floating population, the number of total population and the number of medical institutions were significantly correlated with the spread of urban epidemic. The regions with high epidemic risk index showed the characteristics of large population mobility and relatively dense population. The unstructured and semi-structured multi-source COVID-19 data were extracted, and their correlation with the spread of the epidemic was analyzed. Finally, the reasons for the impact of risk factors on the epidemic risk level and the risk level of each city were analyzed. The results can provide data extraction analysis and reference for relevant departments of epidemic prevention and control decision-making and similar research.
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Index Terms
- Extraction and Analysis of Multi-Source COVID-19 Data based on Deep Learning
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