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

Autor:	Schlee W; Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany. winfried.schlee@gmail.com., Langguth B; Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany., Pryss R; Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany., Allgaier J; Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany., Mulansky L; Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany., Vogel C; Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany., Spiliopoulou M; Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany., Schleicher M; Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany., Unnikrishnan V; Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany., Puga C; Faculty of Computer Science, Otto von Guericke University Magdeburg, Magdeburg, Germany., Manta O; Institute of Communications and Computer Systems (ICCS) - National Technical University of Athens, Athens, Greece., Sarafidis M; Institute of Communications and Computer Systems (ICCS) - National Technical University of Athens, Athens, Greece., Kouris I; Institute of Communications and Computer Systems (ICCS) - National Technical University of Athens, Athens, Greece., Vellidou E; Institute of Communications and Computer Systems (ICCS) - National Technical University of Athens, Athens, Greece., Koutsouris D; Institute of Communications and Computer Systems (ICCS) - National Technical University of Athens, Athens, Greece., Koloutsou K; Sphynx Technology Solutions AG, Zug, Switzerland., Spanoudakis G; Sphynx Technology Solutions AG, Zug, Switzerland., Cederroth C; Laboratory of Experimental Audiology, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm, Sweden.; Hearing Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK.; National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust, Nottingham, UK., Kikidis D; First Department of Otorhinolaryngology, Head and Neck Surgery, National and Kapodistrian University of Athens, Hippokration General Hospital, Athens, Greece.
Jazyk:	angličtina
Zdroj:	Current topics in behavioral neurosciences [Curr Top Behav Neurosci] 2021; Vol. 51, pp. 175-189.
DOI:	10.1007/7854_2021_229
Abstrakt:	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. (© 2021. Springer Nature Switzerland AG.)
Databáze:	MEDLINE
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