Using Denotational Mathematics for the Formal Description of Home UbiHealth Decision-Support Systems With Knowledge Flow

Abstract

Home UbiHealth applications require support from decision-making and decision-support components. The decision process must possess a holistic perspective about the individual's healthcare condition and needs at home. Also, it shall consider and evaluate the available data, producing cognitive intelligence through appropriate processing. The present work describes a model for decision-making and decision-support in UbiHealth environments based on denotational mathematics. The decision-making component of the model provides the necessary actions to handle the connected devices. The decision-support component suggests actions to the user (physician). The decisions are drawn from a simulated operation of cooperating and co-existing semantic networks. The model is thoroughly described, and its positive impact is explained for all participating stakeholders including medical professionals, patients, information technology scientists, and technical personnel.

A Model for Predicting Physical Health of College Students Based on Semantic Web and Deep Learning Under Cloud Edge Collaborative Architecture

“…If the set value is reached, the operation ends; Otherwise, go to Step 3 to continue iterating and updating; 4) Obtain the optimal RBFNN model based on the optimal parameters and input the characteristic values and influencing factors of each indicator into the model to predict the physical test scores of college students, including the physical test scores of college students from 2018 to 2021, as well as the physical test scores of 2023 and 2024. On the basis of the predicted results of physical examinations, the health status of students can be grasped, a finding which also provides technical support for the development of healthcare services (Nguyen et al, 2021;Xiao et al, 2022;Alamer et al, 2022;Vagelatos & Sarivougioukas, 2021;Sarrab & Alshohoumi, 2021).…”