CrowdHEALTH: Big Data Analytics and Holistic Health Records.

The aim of this paper is to present examples of big data techniques that can be applied on Holistic Health Records (HHR) in the context of the CrowdHEALTH project. Real-time big data analytics can be performed on the stored data (i.e. HHRs) enabling correlations and extraction of situational factors between laboratory exams, physical activities, biosignals, medical data patterns, and clinical assessment. Based on the outcomes of different analytics (e.g. risk analysis, pathways mining, forecasting and causal analysis) on the aforementioned HHRs datasets, actionable information can be obtained for the development of efficient health plans and public health policies.

Analyzing SNOMED CT and HL7 terminology binding for semantic interoperability on post-genomic clinical trials.

Current post-genomic clinical trials in cancer involve the collaboration of several institutions. Multi-centric retrospective analysis requires advanced methods to ensure semantic interoperability. In this scenario, the objective of the EU funded INTEGRATE project, is to provide an infrastructure to share knowledge and data in post-genomic breast cancer clinical trials. This paper presents the process carried out in this project, to bind domain terminologies in the area, such as SNOMED CT, with the HL7 v3 Reference Information Model (RIM). The proposed terminology binding follow the HL7 recommendations, but should also consider important issues such as overlapping concepts and domain terminology coverage. Although there are limitations due to the large heterogeneity of the data in the area, the proposed process has been successfully applied within the context of the INTEGRATE project. An improvement in semantic interoperability of patient data from modern breast cancer clinical trials, aims to enhance the clinical practice in oncology.