Today's technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients' problems, to communicate effectively with patients, and to improve healthcare service quality. The first step of building a smart healthcare information system is representing the healthcare data as connected, reachable, and sharable. In order to achieve this representation, ontologies are used to describe the healthcare data. Combining ontological healthcare data with the used and obtained data can be maintained by storing the entire health domain data inside big data stores that support both relational and graph-based ontological data. There are several big data stores and different types of big data sets in the healthcare domain. The goal of this paper is to determine the most applicable ontology data store for storing the big healthcare data. For this purpose, AllegroGraph and Oracle 12c data stores are compared based on their infrastructural capacity, loading time, and query response times. Hence, healthcare ontologies (GENE Ontology, Gene Expression Ontology (GEXO), Regulation of Transcription Ontology (RETO), Regulation of Gene Expression Ontology (REXO)) are used to measure the ontology loading time. Thereafter, various queries are constructed and executed for GENE ontology in order to measure the capacity and query response times for the performance comparison between AllegroGraph and Oracle 12c triple stores.
Results: In the analyses wherein baseline weight was carried forward for missing data, the IBT produced significantly larger mean weight loss in comparison to the EE at the end of the 8 weeks [2.28 kg (2.11) vs. 0.74 kg (1.57), p=0.001]. The participants in the IBT group, when compared to the EE group, were also more likely to achieve a clinically significant weight loss of 5% of their initial body weight at the end of the 8-week study period (17.6% vs. 2%, p=0.016). Conclusion: The participants who received a structured IBT intervention lost significantly more weight after two months, compared to those who received weekly informational emails regarding weight loss. Internet-based behavioral therapy programs may have the potential to serve as a low-cost alternative for obese patients.
<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">This paper describes indexing of ontological data to reduce the memory consumption of a Rete-</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"> <span class="text">based reasoner whose time performance is increased using a hybrid optimization heuristic. The aforementioned indexing mechanism is known as the Pyramid Technique. Our work organizes</span> <span class="text">three dimensional ontological data in a way that works efficiently with this indexing mechanism and it constructs a subset of the querying scheme of the Pyramid Technique that supports querying ontological data. This work also implements an optimization on the Pyramid Technique. Finally, it represents the progress in the memory consumption of the reasoner.</span></span></p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.