Text segmentation of health examination item based on character statistics and information measurement. Issue 1 (29th March 2018) - An, Hui; Wang, Dahui; Pan, Zhigeng; Chen, Meiling; Wang, Xinting
Abstract : This study explores the segmentation algorithm of item text data, especially of single long length data in health examination. In the specific implementation, a large amount of historical health examination data is analysed. Using the method of character statistics, the connection tightness values T AB s between two adjacent characters are calculated. Three parameters, the candidate number N, the best position BP, and balance weight BW are set. The total segmentation indexes SIs are calculated, thus determined the segmentation position Pos. The optimal parameter values are determined by the method of information measurement. Experimental results show that the accuracy rate is 78.6% and reaches 82.9% in the most frequently appeared text item. The complexity of the algorithm is O ( n ). Using no existing domain knowledge, it is very simple and fast. By executed repeatedly, it is convenient to obtain the characteristics of each single item of text data, furthermore, to distinguish respective express preference of different physicians to the same item. The assumption is verified that without professional domain knowledge, a large amount of historical data can provide valuable clues for the text understanding. The results of this research are being applied and verified in the following research works in the field of health examination.