Predicting the optimal therapeutic intervention for tinnitus patients using random forest regression: A preliminary study of UNITI's decision support system model.

Tinnitus is the conscious perception of a phantom sound in absence of an external or internal stimulus. More than 1 in 7 adults in the EU experience tinnitus and for a large proportion of them tinnitus is an intrusive, persistent, and disabling condition, which impairs their life quality. Therefore, tinnitus is posed as a major global burden, which requires a precision-medicine approach in terms of treatments that are tailored to individual patients, due to its high heterogeneity. UNITI is a research and innovation project which aims towards this goal, unifying treatments and interventions for tinnitus. In the context UNITI, a randomized controlled trial (RCT) is being conducted and all the participants' data will be utilized for the development of a clinical decision support system (CDSS). This CDSS will predict the optimal therapeutic intervention for a tinnitus patient based on their profile. In this paper, we present a preliminary study of the CDSS model development process. We describe the available input data, the pre-processing steps conducted, the algorithms tested to model the CDSS' prediction, the models' results, and the future work in the context of this project. The R2 score of the selected model is currently 0.65, indicating that its development process is in the right direction but further tuning and hyperparameter optimization is needed. Clinical Relevance- The proposed model will be integrated in a CDSS aiming at indicating the optimal treatment strategy for a tinnitus patient based their personal profile.

Utilizing Co-Creative Principles to Develop an E-Learning Platform for Interprofessional Training on Tinnitus: The Erasmus+ Project Tin-TRAC.

Tinnitus treatment, diagnosis and management across Europe varies significantly. The lack of national clinical guidelines for tinnitus management in most European countries and the absence of a common language across all disciplines involved is reflected in the diversification of healthcare practices. Interprofessional Training for Tinnitus Researchers and Clinicians (Tin-TRAC) is an Erasmus+ project that aims to develop common educational ground in the form of an e-Learning platform, co-created by patients, researchers and clinicians, which is able to unify tinnitus diagnosis and treatment strategies across Europe. A pan-European thematic educational platform integrating the best practices and latest research achievements with regard to tinnitus diagnosis and management has the potential to act as a facilitator of the reduction of interdisciplinary and interregional practice diversification. A detailed analysis of the educational needs of clinicians and researchers across disciplines will be followed by the co-creative development of the curriculum. Reusable learning objects will incorporate the training contents and will be integrated in an open e-Learning platform. Tin-TRAC envisions that its output will answer the need to create a common language across the clinicians and researchers of different disciplines that are involved in tinnitus management, and reduce patients' prolonged suffering, non-adherence and endless referral trajectories.

Why a Clinical Decision Support System is needed for Tinnitus?

Tinnitus is the perception of a phantom sound and the individual's reaction to it. Although much progress has been made, tinnitus remains an unresolved scientific and clinical issue, affecting more than 10% of the general population and having a high prevalence and socioeconomic burden. Clinical decision support systems (CDSS) are used to assist clinicians in their complex decision-making processes, having been proved that they improve healthcare delivery. In this paper, we present a CDSS for tinnitus, attempting to address the question which treatment approach is optimal for a particular patient based on specific parameters. The CDSS will be developed in the context of the EU-funded "UNITI" project and, after the project completion, it will be able to determine the suitability and expected attachment of a particular patient to a list of available clinical interventions, utilizing predictive and classification machine learning models.Clinical Relevance - The proposed clinically utilizable CDSS will be able to suggest the optimal treatment strategy for the tinnitus patient based on a set of heterogeneous data.

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.

Serious games for Parkinson's Disease management as implemented in PROPHETIC platform.

In this paper, we describe the serious games, integrated into PROPHETIC which is an innovating personal healthcare service for a holistic remote management of Parkinson's disease (PD) patients. The main objective of the three developed serious games is to allow health professionals to remotely monitor and appraise the overall physical status of their patients. The significant benefits for the patients, making use of this platform, is the improvement of their engagement, empowerment and, consequently, the provision of education about their condition and its management. The design of the serious games was based on the clinical needs derived from the literature and their primary target is to assess and record specific physical capabilities of the patient. All the games scores and the recorded parameters are gathered and also presented to the clinicians, offering them a precise overview of the patient's motor status and the possibility to modify the therapeutic plan, if required.

Education Program for Carers in Facilities with Neuro Disabled Subjects EPoCFiNDS.

The prevalence of chronic illness and the disabilities they cause are strongly associated with age. According to the United Nations, in most countries around the world, 8-10% of the population has some form of disability. Carers are helping subjects who have severe or profound core activity limitations in the community and hospice facilities. The skills acquired by carers in their caring role are relevant to the competencies required for occupations and qualifications in community, aged care, health, youth, housing and disability support services. With the aging population the number of subjects with neurological lesions living in hospices and long-term care facilities is increased. It makes a strong case to educate carers to help these subjects. There is a lack of evidence on how to design and implement mechanisms such as foundation skills courses and programs to best meet the needs of carers. The goal of Education Program for Carers in Facilities with Neuro Disabled Subjects (EPoCFiNDS), is to create training programs for carers in neurodisabled subjects living in various facilities. In Europe we need to develop educational programs, aimed at volunteers, relatives or any other group of people so that they better organize benefits care for neurodisabled subjects.