Mary Ellen Grap, Heather C Hamner, Carrie Dooyema, Adi Noiman, Sohyun Park
Factors Associated with Sugar-Sweetened Beverage Intake Among Young Children - United States, 2021
Preventing chronic disease
detecting voice disorders voice recordings could allow frequent remote lowcost screening costly clinical visits invasive laryngoscopy examination goals detect unilateral vocal fold paralysis uvfp voice recordings using machine learning identify acoustic variables important prediction increase trust determine model performance relative clinician performance patients confirmed uvfp endoscopic examination n 77 controls normal voices matched age sex n 77 included voice samples elicited reading rainbow passage sustaining phonation vowel four machine learning models differing complexity used shapley additive explanations shap used identify important features highest median bootstrapped roc auc score 087 beat clinicians performance range 074081 based recordings recording durations different uvfp recordings controls due data originally processed storing show classify groups counterintuitively many uvfp recordings higher intensity controls uvfp patients tend weaker voices revealing datasetspecific bias mitigate additional analysis demonstrate recording biases audio duration intensity created datasetspecific differences patients controls models used improve classification furthermore clinicians ratings provide evidence patients overprojecting voices recorded higher amplitude signal controls interestingly matching audio duration removing variables associated intensity order mitigate biases models able achieve similar high performance provide set recommendations avoid bias building evaluating machine learning models screening laryngology