NextGEM: Next-Generation Integrated Sensing and Analytical System for Monitoring and Assessing Radiofrequency Electromagnetic Field Exposure and Health.

The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM's vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM's Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.

Big Data Assurance: An Approach Based on Service-Level Agreements.

Big data management is a key enabling factor for enterprises that want to compete in the global market. Data coming from enterprise production processes, if properly analyzed, can provide a boost in the enterprise management and optimization, guaranteeing faster processes, better customer management, and lower overheads/costs. Guaranteeing a proper big data pipeline is the holy grail of big data, often opposed by the difficulty of evaluating the correctness of the big data pipeline results. This problem is even worse when big data pipelines are provided as a service in the cloud, and must comply with both laws and users' requirements. To this aim, assurance techniques can complete big data pipelines, providing the means to guarantee that they behave correctly, toward the deployment of big data pipelines fully compliant with laws and users' requirements. In this article, we define an assurance solution for big data based on service-level agreements, where a semiautomatic approach supports users from the definition of the requirements to the negotiation of the terms regulating the provisioned services, and the continuous refinement thereof.

A Modelling Framework for Evidence-Based Public Health Policy Making.

It is widely recognised that the process of public health policy making (i.e., the analysis, action plan design, execution, monitoring and evaluation of public health policies) should be evidenced based, and supported by data analytics and decision-making tools tailored to it. This is because the management of health conditions and their consequences at a public health policy making level can benefit from such type of analysis of heterogeneous data, including health care devices usage, physiological, cognitive, clinical and medication, personal, behavioural, lifestyle data, occupational and environmental data. In this paper we present a novel approach to public health policy making in a form of an ontology, and an integrated platform for realising this approach. Our solution is model-driven and makes use of big data analytics technology. More specifically, it is based on public health policy decision making (PHPDM) models that steer the public health policy decision making process by defining the data that need to be collected, the ways in which they should be analysed in order to produce the evidence useful for public health policymaking, how this evidence may support or contradict various policy interventions (actions), and the stakeholders involved in the decision-making process. The resulted web-based platform has been implemented using Hadoop, Spark and HBASE, developed in the context of a research programme on public health policy making for the management of hearing loss called EVOTION, funded by the Horizon 2020.

Using Big Data to Develop a Clinical Decision Support System for Tinnitus Treatment.

Tinnitus is a common symptom of a phantom sound perception with a considerable socioeconomic impact. Tinnitus pathophysiology is enigmatic and its significant heterogeneity reflects a wide spectrum of clinical manifestations, severity and annoyance among tinnitus sufferers. Although several interventions have been suggested, currently there is no universally accepted treatment. Moreover, there is no well-established correlation between tinnitus features or patients' characteristics and projection of treatment response. At the clinical level, this practically means that selection of treatment is not based on expected outcomes for the particular patient.The complexity of tinnitus and lack of well-adapted prognostic factors for treatment selection highlight a potential role for a decision support system (DSS). A DSS is an informative system, based on big data that aims to facilitate decision-making based on: specific rules, retrospective data reflecting results, patient profiling and predictive models. Therefore, it can use algorithms evaluating numerous parameters and indicate the weight of their contribution to the final outcome. This means that DSS can provide additional information, exceeding the typical questions of superiority of one treatment versus another, commonly addressed in literature.The development of a DSS for tinnitus treatment selection will make use of an underlying database consisting of medical, epidemiological, audiological, electrophysiological, genetic and tinnitus subtyping data. Algorithms will be developed with the use of machine learning and data mining techniques. Based on the profile features identified as prognostic these algorithms will be able to suggest whether additional examinations are needed for a robust result as well as which treatment or combination of treatments is optimal for every patient in a personalized level.In this manuscript we carefully define the conceptual basis for a tinnitus treatment selection DSS. We describe the big data set and the knowledge base on which the DSS will be based and the algorithms that will be used for prognosis and treatment selection.

A Data-informed Public Health Policy-Makers Platform.

Hearing loss is a disease exhibiting a growing trend due to a number of factors, including but not limited to the mundane exposure to the noise and ever-increasing size of the older population. In the framework of a public health policymaking process, modeling of the hearing loss disease based on data is a key factor in alleviating the issues related to the disease and in issuing effective public health policies. First, the paper describes the steps of the data-driven policymaking process. Afterward, a scenario along with the part of the proposed platform responsible for supporting policymaking are presented. With the aim of demonstrating the capabilities and usability of the platform for the policy-makers, some initial results of preliminary analytics are presented in the framework of a policy-making process. Ultimately, the utility of the approach is validated throughout the results of the survey which was presented to the health system policy-makers involved in the policy development process in Croatia.

Application of Big Data to Support Evidence-Based Public Health Policy Decision-Making for Hearing.

Ideally, public health policies are formulated from scientific data; however, policy-specific data are often unavailable. Big data can generate ecologically-valid, high-quality scientific evidence, and therefore has the potential to change how public health policies are formulated. Here, we discuss the use of big data for developing evidence-based hearing health policies, using data collected and analyzed with a research prototype of a data repository known as EVOTION (EVidence-based management of hearing impairments: public health pOlicy-making based on fusing big data analytics and simulaTION), to illustrate our points. Data in the repository consist of audiometric clinical data, prospective real-world data collected from hearing aids and an app, and responses to questionnaires collected for research purposes. To date, we have used the platform and a synthetic dataset to model the estimated risk of noise-induced hearing loss and have shown novel evidence of ways in which external factors influence hearing aid usage patterns. We contend that this research prototype data repository illustrates the value of using big data for policy-making by providing high-quality evidence that could be used to formulate and evaluate the impact of hearing health care policies.

Associations Between Hearing Performance and Physiological Measures - An Overview and Outlook.

The current paper summarises the research investigating associations between physiological data and hearing performance. An overview of state-of-the-art research and literature is given as well as promising directions for associations between physiological data and data regarding hearing loss and hearing performance. The physiological parameters included in this paper are: electrodermal activity, heart rate variability, blood pressure, blood oxygenation and respiratory rate. Furthermore, the environmental and behavioural measurements of physical activity and body mass index, alcohol consumption and smoking have been included. So far, only electrodermal activity and heart rate variability are physiological signals simultaneously associated with hearing loss or hearing performance. Initial findings suggest blood pressure and respiratory rate to be the most promising physiological measures that relate to hearing loss and hearing performance.