An exploration of psychological symptom-based phenotyping of adult cochlear implant users with and without tinnitus using a machine learning approach

Abstract

The identification of phenotypes within populations with troublesome tinnitus is an important step towards individualizing tinnitus treatments to achieve optimal outcomes. However, previous application of clustering algorithms has called into question the existence of distinct tinnitus-related phenotypes. In this study, we attempted to characterize patients' symptom-based phenotypes as subpopulations in a Gaussian mixture model (GMM), and subsequently performed a comparison with tinnitus reporting. We were able to effectively evaluate the statistical models using cross-validation to establish the number of phenotypes in the cohort, or a lack thereof. We examined a cohort of adult cochlear implant (CI) users, a patient group for which a relation between psychological symptoms (anxiety, depression, or insomnia) and trouble tinnitus has previously been shown. Accordingly, individual item scores on the Hospital Anxiety and Depression Scale (HADS; 14 items) and the Insomnia Severity Index (ISI; 7 items) were selected as features for training the GMM. The resulting model indicated four symptom-based subpopulations, some primarily linked to one major symptom (e.g., anxiety), and others linked to varying severity across all three symptoms. The presence of tinnitus was self-reported and tinnitus-related handicap was characterized using the Tinnitus Handicap Inventory. Specific symptom profiles were found to be significantly associated with CI users' tinnitus characteristics. GMMs are a promising machine learning tool for identifying psychological symptom-based phenotypes, which may be relevant to determining appropriate tinnitus treatment.

Keywords: Anxiety; Clustering; Depression; Fuzzy; Gaussian; Insomnia; Machine learning; Tinnitus.