A linked data-based approach for clinical treatment selecting support

Abstract

Treatment plan selection is a complex process because it sometimes needs sufficient experience and clinical information. Nowadays it is even harder for doctors to select an appropriate treatment plan for certain patients since doctors might encounter difficulties in obtaining the right information and analyzing the diverse clinical data. In order to improve the effectiveness of clinical decision making in complicated information system environments, we first propose a linked data-based approach for treatment plan selection. The approach integrates the patients' clinical records in hospitals with open linked data sources out of hospitals. Then, based on the linked data net, treatment plan selection is carried on aided by similar historical therapy cases. Finally, we reorganize the electronic medical records of 97 colon cancer patients using the linked data model and count the similarity of these records to help treatment selecting. The experiment shows the usability of our method in supporting clinical decisions.

A new approach for image databases design

“…Another example is in industrial applications [1] or called industrial information integration [47][48][49]; the majority of data exchanges and communication are based on visual data, available three dimensional (3D) scanners and computer aided design (CAD) and computer aided manufacturing (CAM) tools have accelerated the growth rate of image data greatly [2-4, 10, 43, 48, 50, 51, 53, 54]. The explosive growth of image data from digital devices is making database management extremely tedious and clumsy tasks [5,20,34,52]. Correspondingly, the techniques for organizing, storing, processing, and retrieving multimedia data are becoming more and more important.…”

An IoT-based risk warning system for smart libraries

“…Accordingly, this paper proposes a risk warning system (Yang, Li, Ji and Xu, 2001; Yang, Li, Xie and Xu, 2001) based on IoT technologies with fuzzy algorithms. Case-based reasoning (CBR) (Jiang et al , 2014; Yang, Li, Ji and Xu, 2001; Yang, Li, Xie and Xu, 2001) are applied for data processing and analytics. Experiments have been conducted to test the proposed system.…”