Health data integration enables a collaborative utilization of data across different systems. It not only provides a comprehensive view of a patient's health but can also potentially cope with challenges faced by the current healthcare system. In this literature review, we investigated the existing work on heterogeneous health data integration as well as the methods of utilizing the integrated health data. Our search was narrowed down to 32 articles for analysis. The integration approaches in the reviewed articles were classified into three classifications, and the utilization approaches were classified into five classifications. The topic of health data integration is still under debate and problems are far from being resolved. This review suggests the need for a more efficient way to invoke the various services for aggregating health data, as well as a more effective way to integrate the aggregated health data for supporting collaborative utilization. We have found that the combination of Web Application Programming Interface and Semantic Web technologies has the potential to cope with the challenges based on our analysis of the review result.

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

Various health Web services host a huge amount of health data about patients. The heterogeneity of the services hinders the collaborative utilization of these health data, which can provide a valuable support for the self-management of chronic diseases. The combination of REST Web services and Semantic Web technologies has proven to be a viable approach to address the problem. This paper proposes a method to add semantic annotations to the REST Web services. The service descriptions and the resource representations with semantic annotations can be transformed into a resource graph. It integrates health data from different services, and can link to the health-domain ontologies and Linked Open Health Data to support health management and imaginative applications. The feasibility of our method is demonstrated by realizing with OpenAPI service description and JSON-LD representation in an example use case.

The utilization of Web services is becoming a human labor consuming work as the rapid growth of Web. The semantic annotated service description can support more automatic ways on tasks such as serv ...

The vast amount of Web services brings the problem of discovering desired services for composition and orchestration. The syntactic service matching methods based on the classical set theory have a difficulty to capture the imprecise information. Therefore, an approximate service matching approach based on fuzzy control is explored in this paper. A service description matching model to the OpenAPI specification, which is the most widely used standard for describing the defacto REST Web services, is proposed to realize the fuzzy service matching with the fuzzy inference method developed by Mamdani and Assilian. An evaluation shows that the fuzzy service matching approach performed slightly better than the weighted approach in the setting context.