Clinical Decision Support Systems for Comorbidity: Architecture, Algorithms, and Applications

Abstract

In this paper, we present the design of a clinical decision support system (CDSS) for monitoring comorbid conditions. Specifically, we address the architecture of a CDSS by characterizing it from three layers and discuss the algorithms in each layer. Also we address the applications of CDSSs in a few real scenarios and analyze the accuracy of a CDSS in consideration of the potential conflicts when using multiple clinical practice guidelines concurrently. Finally, we compare the system performance in our design with that in the other design schemes. Our study shows that our proposed design can achieve a clinical decision in a shorter time than the other designs, while ensuring a high level of system accuracy.

Secure Decision-Making Approach to Improve Knowledge Management Based on Online Samples

“…9. It is inferred that the throughput gets increased for the proposed DM-GT than the existing algorithms such as MEWS [34], and CDSS [35].…”

“…Medical Intuitionistic Fuzzy Expert Decision Support System (MIFEDSS) helps in dragonising the data related to health care and decision-making is processed and recorded [34]. In the decision-making process, [35] discuss clinical applications and analyze the accuracy during the multiple data conflict.…”

“…8. It is inferred that the transmission delay gets decreased for the proposed DM-GT than the existing algorithms such as MEWS [34], and CDSS [35]. The throughput is calculated by varying the number of inputs (Data) as shown in Fig.…”