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Tinnitus is the perception of a phantom sound and the patient's reaction to it. Although much progress has been made, tinnitus remains a scientific and clinical enigma of high prevalence and high economic burden, with an estimated prevalence of 10%-20% among the adult population. The EU is funding a new collaborative project entitled "Unification of Treatments and Interventions for Tinnitus Patients" (UNITI, grant no. 848261) under its Horizon 2020 framework. The main goal of the UNITI project is to set the ground for a predictive computational model based on existing and longitudinal data attempting to address the question of which treatment or combination of treatments is optimal for a specific patient group based on certain parameters. Clinical, epidemiological, genetic and audiological data, including signals reflecting ear-brain communication, as well as patients' medical history, will be analyzed making use of existing databases. Predictive factors for different patient groups will be extracted and their prognostic relevance validated through a Randomized Clinical Trial (RCT) in which different patient groups will undergo a combination of tinnitus therapies targeting both auditory and central nervous systems. From a scientific point of view, the UNITI project can be summarized into the following research goals: (1) Analysis of existing data: Results of existing clinical studies will be analyzed to identify subgroups of patients with specific treatment responses and to identify systematic differences between the patient groups at the participating clinical centers. (2) Genetic and blood biomarker analysis: High throughput Whole Exome Sequencing (WES) will be performed in well-characterized chronic tinnitus cases, together with Proximity Extension Assays (PEA) for the identification of blood biomarkers for tinnitus. (3) RCT: A total of 500 patients will be recruited at five clinical centers across Europe comparing single treatments against combinational treatments. The four main treatments are Cognitive Behavioral Therapy (CBT), hearing aids, sound stimulation, and structured counseling. The consortium will also make use of e/m-health applications for the treatment and assessment of tinnitus. (4) Decision Support System: An innovative Decision Support System will be implemented, integrating all available parameters (epidemiological, clinical, audiometry, genetics, socioeconomic and medical history) to suggest specific examinations and the optimal intervention strategy based on the collected data. (5) Financial estimation analysis: A cost-effectiveness analysis for the respective interventions will be calculated to investigate the economic effects of the interventions based on quality-adjusted life years. In this paper, we will present the UNITI project, the scientific questions that it aims to address, the research consortium, and the organizational structure.

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.