@Article{jnm.2020.010135,
AUTHOR = {Panjie Yang, Gang Liu, Xiaoyu Li, Liyuan Qin, Xiaoxia Liu},
TITLE = {An Intelligent Tumors Coding Method Based on Drools},
JOURNAL = {Journal of New Media},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {111--119},
URL = {http://www.techscience.com/JNM/v2n3/40102},
ISSN = {2579-0129},
ABSTRACT = {In order to solve the problems of low efficiency and heavy workload of 
tumor coding in hospitals, we proposed a Drools-based intelligent tumors coding 
method. At present, most tumor hospitals use manual coding, the trained coders 
follow the main diagnosis selection rules to select the main diagnosis from the 
discharge diagnosis of the tumor patients, and then code all the discharge 
diagnoses according to the coding rules. Owing to different coders have different 
familiarity with the main diagnosis selection rules and ICD-10 disease coding, it 
will reduce the efficiency of the artificial coding results and affect the quality of 
the whole medical record. We first analyze the ICD library information, doctor's 
diagnostic information, radiotherapy information or chemotherapy information, 
surgery information, hospitalization information and other related information, 
and then generated Drools rule files based on the main diagnostic selection 
principles and coding principles, we also combined the text similarity analysis 
algorithm to construct an intelligent diagnostic information coding method. 
Practice shows that the coding method can be used to make the work efficiently 
and at the same time obtain the coding results which meet the standard and have 
high accuracy, so that the coders can be free from the repeated work and pay more 
attention to coding quality control and the coding logic adjustment.},
DOI = {10.32604/jnm.2020.010135}
}