JMIR Publications

ABSTRACT

Background:

Lifestyle diseases, because of adverse health behavior, are the foremost cause of death worldwide. An eCoach system may encourage individuals to lead a healthy lifestyle with health risk prediction, personalized recommendation generation, and goal evaluation. Such an eCoach system needs to collect and transform distributed heterogenous health and wellness data into meaningful information to train an artificially intelligent health risk prediction model. But it may produce data compatibility dilemma. Our proposed eHealth ontology can increase interoperability between different heterogeneous networks, give situation awareness, help in data integration, and discover inferred knowledge. This “proof of concept (POC)” study will help sensor, questionnaire, and interview data to be more organized for health risk prediction and personalized recommendation generation targeting obesity as a study case.

Objective:

The aim of this study has been an OWL-based ontology (called the “UiA eHealth Ontology/UiAeHo”) to annotate personal, physiological, behavioral and contextual data from heterogeneous sources (sensor, questionnaire, and interview), and followed by, structuring and standardizing of diverse descriptions to generate meaningful, practical, personalized, and contextual lifestyle recommendations based on the defined rules.

Methods:

We have developed a Java-based simulator to collect dummy personal, physiological, behavioral, and contextual data related to artificial participants involved in health monitoring. We have integrated the concepts of “SSN Ontology”, and “SNOMED-CT” to develop our proposed eHealth ontology. The ontology has been created using Protégé (V. 5.x). Following, we have used the Java-based “Jena Framework” (V. 3.16) for building a semantic web application that includes RDF API, OWL API, native tuple store (TDB), and the SPARQL query engine. The logical and structural consistency of the proposed ontology has been performed with “HermiT 1.4.3.x” ontology reasoner available in Protégé 5.x.

Results:

The proposed ontology has been implemented for the study case “Obesity”. However, it can be extended further for other lifestyle diseases. “UiA eHealth Ontology” has been constructed using 623 logical axioms, 363 declaration axioms, 162 classes, 83 object properties, and 101 data properties. The ontology can be visualized with “Owl Viz”, and the formal representation has been used to infer a participant's health status using the “HermiT” reasoner. In addition, we have developed a Java-based module for ontology verification, that behaves like a rule-based decision support system (DSS) to predict the probability for health risk, based on the evaluation of the results obtained from SPARQL queries. Moreover, we have discussed the potential lifestyle recommendation generation plan against adverse behavioral risks.

Conclusions:

This study has led to the creation of a meaningful, context-specific ontology to model massive, unintuitive raw, unstructured observations for health and wellness data (e.g., sensors, interviews, questionnaires) and to annotate them with semantic metadata to create a compact, intelligible abstraction for health risk predictions for individualized recommendation generation.