Apostolova Trpkovska Marika, Yildirim Yayilgan Sule, Besimi Adrian
South East European University, Ilindenska n. 335, 1200 Tetovo, Macedonia.
Norwegian University of Science and Technology, 2815 Gjøvik, Norway.
Stud Health Technol Inform. 2016;221:69-73.
This paper proposes a social network with an integrated children disease prediction system developed by the use of the specially designed Children General Disease Ontology (CGDO). This ontology consists of children diseases and their relationship with symptoms and Semantic Web Rule Language (SWRL rules) that are specially designed for predicting diseases. The prediction process starts by filling data about the appeared signs and symptoms by the user which are after that mapped with the CGDO ontology. Once the data are mapped, the prediction results are presented. The phase of prediction executes the rules which extract the predicted disease details based on the SWRL rule specified. The motivation behind the development of this system is to spread knowledge about the children diseases and their symptoms in a very simple way using the specialized social networking website www.emama.mk.
本文提出了一个社交网络，该网络带有一个通过使用专门设计的儿童常见疾病本体（CGDO）开发的综合儿童疾病预测系统。这个本体由儿童疾病及其与症状的关系以及专门为疾病预测设计的语义网规则语言（SWRL规则）组成。预测过程始于用户填写出现的体征和症状数据，这些数据随后会与CGDO本体进行映射。一旦数据被映射，就会呈现预测结果。预测阶段执行规则，根据指定的SWRL规则提取预测疾病的详细信息。开发这个系统背后的动机是通过专门的社交网站www.emama.mk以非常简单的方式传播有关儿童疾病及其症状的知识。
