Based on the detailed D2.1 report, this document extends state of the art in the realization of the mechanisms and algorithms of the CrowdHEALTH platform – a secure ICT platform that incorporates the collective knowledge from the multiple heterogeneous sources and its combination with situational awareness artefacts- based on holistic health records, heterogeneous data aggregation systems and algorithms, big data analysis and storage, mining, forecasting and visualisation, and finally policy development toolkits.
This document is the causal analysis framework of the health policies toolkit and it will be used as the base for the development of the software prototype that applies to the health analytics layer in CrowdHEALTH architecture (D5.15). This framework is focusing on the analysis of actions and events in the Use Case scenarios aiming to estimate the applicability and the effectiveness of the current health policies referring to the specific case.
This document presents deliverable D6.7 Use Case Scenarios Definition and Design v1 of Work Package 6, Use Cases Adaptation, Integration and Experimentation. The main objective is to provide the Use Case Scenarios definition and specification as well as to present the scenarios in conjunction with the identification of the involved stakeholders that are vital for the deployment of the CrowdHEALTH platform. In this deliverable, we aim to describe the representative use case scenarios for the CrowdHEALTH project.
The present document describes the results of the first development and integration cycle of CrowdHEALTH, as well as the work performed to achieve such results. Achievements are compared to the integration plan reported in D6.1, describing the level of completeness of the developed functionalities and integration of each component, highlighting possible delays or differences with respect to the plan, and reporting the most important issues already solved or still to be solved during this activities, when occurred.
This document is the first in a series of deliverables on data-driven analytical tools for supporting policy makers develop healthcare policies. The focus here is on population-level risk stratification, employing machine learning tools for stratifying segments of the population into different levels of risk (low, medium, high).
The aim of this Deliverable is to define the concept of Public Health Policy (PHP) and present a state-of-the-art on PHPs development, and to propose a first approach to the modelling and evaluation of PHPs that will be used in the Policy Development Toolkit (PDT) to support PHPs evaluation and development for policy-makers.
This document is part of the WP4 Information and Knowledge Acquisition and Management of the CrowdHEALTH project. The purpose of this report is to describe the current status of the Holistic Security and Privacy Framework of CrowdHEALTH, which is crucial for the protection of the CrowdHEALTH’s resources and data. This document presents briefly the regulatory requirements of CrowdHEALTH, and presents the technologies and protocols that will be used to fulfil the relevant requirements.
The Information Aggregation (IA) component enables the aggregation of different information sources to support the creation of Holistic Health Records (HHRs). The IA component handles streaming and batch data coming from various sources in a scalable, efficient and reliable manner to create Holistic Health Records (HHRs). In this respect, the goal of the IA component is to combine a number of disparate data sources into a common format and to store information in a form that makes it easily and readily available for analytics, simulations and decision making.
One of the biggest issues to achieve full semantic interoperability in healthcare in which all the systems seamlessly communicate with each other is still pending. In an ideal environment, the patient would have all the clinical information coming from heterogeneous providers integrated and available in a common format. For the stakeholders, it would avoid missing or duplication of clinical information, reducing the hospitals costs. For patients, it would imply better diagnosis and treatment likewise reducing visits to hospitals thus improving their daily live, anywhere at any time.
The purpose of D3.19 is to document the preliminary efforts undertaken within the context of Task 3.5 Data Cleaning including Sources Reliability Assessment. Towards this end, the scope of the current deliverable is to document the architecture and design of the Data Cleaner & Sources Verifier component and the mechanisms that will be used in order to address the volatility of the information provision, as well as the reliability of the data sources, within the context of CrowdHEALTH.