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

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.PMID:33637224 | DOI:10.1016/bs.pbr.2020.10.002
Source: Brain Research - Category: Neurology Authors: Source Type: research