Machine Learning and Comorbidity-Based Investigations into the Genetics of Stuttering
dc.contributor.advisor | Below, Jennifer E | |
dc.creator | Shaw, Douglas | |
dc.date.accessioned | 2024-05-15T16:39:31Z | |
dc.date.created | 2024-05 | |
dc.date.issued | 2024-03-04 | |
dc.date.submitted | May 2024 | |
dc.identifier.uri | http://hdl.handle.net/1803/18851 | |
dc.description.abstract | Developmental stuttering is a speech disorder characterized by disruption in the forward movement of speech. This disruption includes part-word and single-syllable repetitions, prolongations, and involuntary tension that blocks syllables and words, and the disorder has a life-time prevalence of 6-12%. Within Vanderbilt's electronic health record (EHR)-linked biorepository (BioVU), only 142 individuals out of 92,762 participants (0.15%) are identified with diagnostic ICD9/10 codes, suggesting a large portion of people who stutter do not have a record of diagnosis within the EHR. To identify individuals affected by stuttering within our EHR, we built a PheCode-driven and genetic risk models to impute the stuttering phenotype in Vanderbilt’s biobank and model phenotypic and genotypic risk in BioVU for the purpose of genetic discovery. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | genetics, stuttering | |
dc.title | Machine Learning and Comorbidity-Based Investigations into the Genetics of Stuttering | |
dc.type | Thesis | |
dc.date.updated | 2024-05-15T16:39:31Z | |
dc.type.material | text | |
thesis.degree.name | PhD | |
thesis.degree.level | Doctoral | |
thesis.degree.discipline | Human Genetics | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
local.embargo.terms | 2024-11-01 | |
local.embargo.lift | 2024-11-01 | |
dc.creator.orcid | 0000-0001-8789-0226 | |
dc.contributor.committeeChair | Samuels, David |
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