A Semantic Big Data Platform for Integrating Heterogeneous Wearable Data in Healthcare

Abstract

Advances supported by emerging wearable technologies in healthcare promise patients a provision of high quality of care. Wearable computing systems represent one of the most thrust areas used to transform traditional healthcare systems into active systems able to continuously monitor and control the patients' health in order to manage their care at an early stage. However, their proliferation creates challenges related to data management and integration. The diversity and variety of wearable data related to healthcare, their huge volume and their distribution make data processing and analytics more difficult. In this paper, we propose a generic semantic big data architecture based on the "Knowledge as a Service" approach to cope with heterogeneity and scalability challenges. Our main contribution focuses on enriching the NIST Big Data model with semantics in order to smartly understand the collected data, and generate more accurate and valuable information by correlating scattered medical data stemming from multiple wearable devices or/and from other distributed data sources. We have implemented and evaluated a Wearable KaaS platform to smartly manage heterogeneous data coming from wearable devices in order to assist the physicians in supervising the patient health evolution and keep the patient up-to-date about his/her status.

Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

“…In parallel, the widespread adoption of electronic health records 6 (EHR*) has also generated massive amounts of digitized personal health information, as has the increasing popularity of automatic serial data acquisition from wearable devices/technologies* 7 and web applications that collect patient-reported health information* (e.g., the www.HepCure.org portal for hepatitis C patients and their physicians). Unprocessed clinical trial data will also soon become more widely accessible.…”

Remote Blood Glucose Monitoring in mHealth Scenarios: A Review

“…Addressing this issue poses several challenges, ranging from the development of new communication architectures supporting the massive usage of cloud computing [60], to the integration of heterogenous data coming from different sources [61] so that interpretation may be accomplished mostly automatically using ontology and terminology servers implemented as distributed agents [16]. Finally, on the regulatory side, many questions are still open concerning the storage and the exchange of sensitive data such as those originated by medical devices.…”

Semantic Mediation Model to Promote Improved Data Sharing Using Representation Learning in Heterogeneous Healthcare Service Environments

“…Data interoperability has been investigated from diverse perspectives in the existing literature; some approaches provide the standard semantic ontology models [28,29] to mitigate the heterogeneity, while others use the semantic alignment [30] approaches to mediate among different models. Also, common data models have been utilized by some organizations to minimize heterogeneity in healthcare applications.…”