Heart Health Risk Assessment System: A Nonintrusive Proposal Using Ontologies and Expert Rules

Abstract

According to the World Health Organization, the world's leading cause of death is heart disease, with nearly two million deaths per year. Although some factors are not possible to change, there are some keys that help to prevent heart diseases. One of the most important keys is to keep an active daily life, with moderate exercise. However, deciding what a moderate exercise is or when a slightly abnormal heart rate value is a risk depends on the person and the activity. In this paper we propose a context-aware system that is able to determine the activity the person is performing in an unobtrusive way. Then, we have defined ontology to represent the available knowledge about the person (biometric data, fitness status, medical information, etc.) and her current activity (level of intensity, heart rate recommended for that activity, etc.). With such knowledge, a set of expert rules based on this ontology are involved in a reasoning process to infer levels of alerts or suggestions for the users when the intensity of the activity is detected as dangerous for her health. We show how this approach can be accomplished by using only everyday devices such as a smartphone and a smartwatch.

An Ontological Approach for Dental Extraction Decision Making and Knowledge Dissemination – A Pilot Study for Dental Extraction Forceps

“…Using ontologies for modelling knowledge in the health sector is an active research field (Garcia-Valverde et al, 2014). An ontology provides a way to compile medical knowledge in a reusable, shareable, machine-understandable way (Park et al, 2007).…”

“…Even though health sector is being supported by number of biomedical ontologies such as GALEN, the Unified Medical Language Source, the Systemic Nomenclature of Medicine which focus on general scope of the biomedical domain (Kuziemsky and Lau, 2010) and the Gene Ontology (GO) which is one of the earliest and most frequently used vocabularies (Hu et al, 2016), there are a very few ontology on dental domain on the health sector. Since the ontology-based systems handle reusable terminology resources, they can be used to improve the management of complex systems for various contextual information (Garcia-Valverde et al, 2014).…”

Context Relevant Prediction Model for COPD Domain Using Bayesian Belief Network

“…This feature has been applied to a number of hospital projects, such as Bhattacharyya et al [ 38 ] that discussed the influence of the patient activity, the home environment, and relaxation on the progression of brain tumor which is reflected in the type of provided care. In a related development, Garcia-Valverde et al [ 39 ] proposed a context-aware system that is able to determine the kind of activities that do not pose a grave threat to the lives of heart diseases patients. Similarly, Kennedy et al [ 40 ] addressed how patients manage their conditions according to their local context, socioeconomic circumstances and domestic and family arrangements.…”

Lynx: Automatic Elderly Behavior Prediction in Home Telecare

“…Ontology itself may be used as a complex set of concepts joined in a parent-child structure, where each concept has been linked with some possible synonym, being most useful to resolve semantic conflicts of ambiguity in a concrete context. To be able to prioritize, we are using a rule-based system that has facility for explaining inferences as a framework for supporting clinicians at the point of care [ 23 ]. Given the large amount of information to be managed, we need to use storage techniques able to manage efficiently large volumes of data (data warehouse) and provide easy access and efficient management of the information (big data paradigm) [ 24 ].…”