G. Sunil Santhosh Kumar^1,2,*, M. Rudra Kumar³

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 695-713, 2024, DOI:10.32604/cmc.2024.050143 - 18 July 2024

Abstract This article presents an innovative approach to automatic rule discovery for data transformation tasks leveraging XGBoost, a machine learning algorithm renowned for its efficiency and performance. The framework proposed herein utilizes the fusion of diversified feature formats, specifically, metadata, textual, and pattern features. The goal is to enhance the system’s ability to discern and generalize transformation rules from source to destination formats in varied contexts. Firstly, the article delves into the methodology for extracting these distinct features from raw data and the pre-processing steps undertaken to prepare the data for the model. Subsequent sections expound… More >

Panjie Yang^1,*,#, Gang Liu^2,#, Xiaoyu Li^1,*, Liyuan Qin¹, Xiaoxia Liu³

Journal of New Media, Vol.2, No.3, pp. 111-119, 2020, DOI:10.32604/jnm.2020.010135 - 04 September 2020

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… More >