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Assessing the Heterogeneity of Complaints Related to Tinnitus and Hyperacusis from an Unsupervised Machine Learning Approach: An Exploratory Study
Author(s) -
Guillaume Palacios,
Arnaud Noreña,
Alain Londero
Publication year - 2020
Publication title -
audiology and neurotology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.106
H-Index - 78
eISSN - 1421-9700
pISSN - 1420-3030
DOI - 10.1159/000504741
Subject(s) - hyperacusis , latent dirichlet allocation , topic model , psychology , computer science , artificial intelligence , cognitive psychology , tinnitus , machine learning , psychiatry
Subjective tinnitus (ST) and hyperacusis (HA) are common auditory symptoms that may become incapacitating in a subgroup of patients who thereby seek medical advice. Both conditions can result from many different mechanisms, and as a consequence, patients may report a vast repertoire of associated symptoms and comorbidities that can reduce dramatically the quality of life and even lead to suicide attempts in the most severe cases. The present exploratory study is aimed at investigating patients' symptoms and complaints using an in-depth statistical analysis of patients' natural narratives in a real-life environment in which, thanks to the anonymization of contributions and the peer-to-peer interaction, it is supposed that the wording used is totally free of any self-limitation and self-censorship.

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