CIDGMed: Causal Inference-Driven Medication Recommendation with enhanced dual-granularity learning
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The information entropy between coarse granularity and fine granularity is comparatively studied,the influence on decision tree caused by coarse granularity and fine granularity is investigated,and the conclusion is provided that the information entropy under coarse granularity is not less than the one under fine granularity.It is shown that the decision tree generated by selecting the expanded attribute under fine granularity is better than the one under coarse granularity.
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This paper discusses a mining problem of approximate periodicity with multi-granularity time in the temporal database. It introduces the concepts and properties of the multi-granularity time interval on the basis of multi-granularity time and multi-granularity time format. It constructs multi-granularity approximate periodic pattern. It proposes an mining algorithm based on self-organizing map to find multi-granularity approximate periodic pattern. Results obtained from experiments on high frequency stock market data of 580000 Bao Steel JBT1 demonstrate that the proposed algorithm is efficient.
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A new mathematical formula for colour granularity has recently been proposed by Saunders1. Whereas some previously derived mathematical expressions of colour granularity have proved difficult to apply in practice due to the existence of terms having no real physical correlate, this new formula relates granularity to easily measured film parameters. Using this formula, successful granularity predictions have been made for a series of single-laver colour negative structures in which the coupler silver halide ratio was systematically varied.
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An equation to describe the granularity of an electrophotographic imaging system has been developed to include the density variance attributed to the reflectance of paper and toner surfaces. Granularity measurements from two conventional electrophotographic copiers were used to validate the relationship. The results show good agreement with the overall shape of the granularity-versus-density curve; however, the values are not consistent with estimated toner particle size. Clustering of the toner particles is suggested as the cause of the high granularity values.
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In recent years, the cancer incidence rate has generally increased on a global scale and there are around a thousand anticancer drugs available in the market now. Doctors can only choose effective drugs based on existing knowledge and experience without automated tools. The granularity selection about the drugs is consistent with multi-granularity decision making system for optimal granularity. The system mainly stems from two parts, global and local optimal granularity selections. This paper proposes to use the tree structure to select the global optimal granularity by improving traditional method and providing the specific algorithm. Parallel processing is conducted to solve the long-time consumption problems. To conclude is the tree structure can efficiently save plenty of time to solve the global optimal granularity problems by comparative analysis. Besides, the larger the amount of patient data, the more time saved. The local optimal granularity selection with parallel processing can save nearly half of the time, which provides automated aids and more time for doctors to choose better anticancer drugs.
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