Information extraction technology can extract the key information in free-text electronic medical records, helping the information management and subsequent information analysis of the hospital. Therefore, the main process of free-text electronic medical record information extraction was simply introduced, the research results of single extraction and joint extraction methods for three most important types of information:named entity, entity assertion and entity relation in the past few years were studied, and the methods, datasets, and final effects of these results were compared and summarized. In addition, an analysis of the features, advantages and disadvantages of several popular new methods, a summarization of commonly used datasets in the field of information extraction of free-text electronic medical records, and an analysis of the current status and research directions of related fields in China was carried out.

When the Log-Structured Merge-tree (LSM-tree) in the time series database is under high write load or resource constraints, file compaction not in time will cause a large accumulation of LSM C ₀ layer data, resulting in an increase in the latency of ad hoc queries of recently written data. To address this problem, a two-stage LSM compaction framework was proposed that realizes low-latency query of newly written time series data on the basis of maintaining efficient query for large blocks of data. Firstly, the file compaction process was divided into two stages:quickly merging of a small number of out-of-order files, merging of a large number of small files, then multiple file compaction strategies were provided in each stage, finally the two-stage compaction resource allocation was performed according to the query load of the system. By implementing the test of the traditional LSM compaction strategy and the two-stage LSM compaction framework on the time series database Apache IoTDB, the results showed that compared with the traditional LSM, the two-stage file compaction module was able to greatly reduce the number of ad hoc query reads while improving the flexibility of the strategy, and made the historical data analysis and query performance improved by about 20%. Experimental results show that the two-stage LSM compaction framework can increase the ad hoc query efficiency of recently written data, and can improve the performance of historical data analysis and query as well as the flexibility of compaction strategy.

In the study of personalized facial attractiveness, due to lack of features and insufficient consideration of the influence factors of public aesthetics, the prediction of personal preferences cannot reach high prediction accuracy. In order to improve the prediction accuracy, a new personalized facial attractiveness prediction framework based on feature-level and decision-level information fusion was proposed. Firstly, the objective characteristics of different facial beauty features were fused together, and the representative facial attractive features were selected by a feature selection algorithm, the local and global features of face were fused by different information fusion strategies. Then, the traditional facial features were fused with the features extracted automatically through deep networks. At the same time, a variety of fusion strategies were proposed for comparison. The score information representing the public aesthetic preferences and the personalized score information representing the individual preferences were fused at the decision level. Finally, the personalized facial attractiveness prediction score was obtained. The experimental results show that, compared with the existing algorithms for personalized facial attractiveness evaluation, the proposed multi-level fusion method has a significant improvement in prediction accuracy, and can achieve the Pearson correlation coefficient more than 0.9. The proposed method can be used in the fields of personalized recommendation and face beautification.