2026-10-12T01:05:10Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34026817 2026-09-29T17:38:03Z category_21 category_272 category_734 category_931 category_106 portal_5 item_type_6 month_year_09_2026

<p>Sensitivity analysis for the VFSS-confirmed cohort (n = 290).</p> Rafaela Soares Rech (10401881) Carla Diniz Lopes Becker (25138426) Dieine Estela Bernieri Schiavon (25138429) Danielli Pires Vieira (25138432) Juliana Balbinot Hilgert (6131699) Fernando Neves Hugo (4829652) Biotechnology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified Science Policy xlink "> early predict whether individuals final four best crucial first stage convolutional neural networks convolutional neural network 15 items related support vector machine oral health characteristics confirmed dysphagia based oropharyngeal dysphagia symptoms machine learning model artificial intelligence based videofluoroscopy swallowing study artificial intelligence tested based different machine theoretical model symptoms identified study aims learning approaches confirm dysphagia speech therapist sensitivity 0 random forest performing models pandas library nearest neighbors naive bayes multilayer perceptron logistic regression future studies externally validate exploratory cross eight algorithms effective management diagnostic accuracy decision tree data visualization clinical assessment accuracy 0 74 ). <p>Sensitivity analysis for the VFSS-confirmed cohort (n = 290).</p> 2026-09-29T17:37:59Z Text Journal contribution 10.1371/journal.pdig.0001755.s003 https://figshare.com/articles/journal_contribution/_p_Sensitivity_analysis_for_the_VFSS-confirmed_cohort_n_290_p_/34026817 CC BY 4.0