{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T23:31:44Z","timestamp":1792539104749,"version":"4.5.0"},"reference-count":45,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T00:00:00Z","timestamp":1675900800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>Hearing loss (HL) is highly prevalent, yet underrecognized and underdiagnosed. Lack of standardized screening, awareness, cost, and access to hearing testing present barriers to HL identification. To facilitate prescreening and selection of patients who warrant audiometric evaluation, we developed a machine learning (ML) model to predict speech\u2010frequency pure\u2010tone average (PTA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Design<\/jats:title><jats:p>Cross\u2010sectional study.<\/jats:p><\/jats:sec><jats:sec><jats:title>Setting<\/jats:title><jats:p>National Health and Nutrition Examination Survey (NHANES).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The cohort included 8918 adults (\u226520 years) who completed audiometric testing with NHANES (2012\u20102018). The primary outcome measure was the prediction of better hearing ear speech\u2010frequency PTA. Relevant predictors included demographics, medical conditions, and subjective assessment of hearing. Supervised ML with a tree\u2010based architecture was used. Regression performance was determined by the mean absolute error (MAE) with binary classification assessed with area under the receiver operating characteristic curve (AUC).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Using the full set of predictors, the test set MAE between the ML\u2010predicted and actual PTA was 5.29\u2009dB HL (95% confidence interval [CI]: 4.97\u20105.61). The 5 most influential predictors of higher PTA were increased age, worse subjective hearing, male gender, increased body mass index, and history of smoking. The 5\u2010factor abbreviated model performed comparably to the extended feature set with MAE 5.36 (95% CI: 5.03\u20105.69) and AUC for PTA\u2009&gt;\u200925\u2009dB HL of 0.92 (95% CI: 0.90\u20100.94).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The ML model was able to predict PTA with patient demographics, clinical factors, and subjective hearing status. ML\u2010based prediction may be used to identify individuals who could benefit most from audiometric evaluation.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ohn.288","type":"journal-article","created":{"date-parts":[[2023,2,9]],"date-time":"2023-02-09T19:53:40Z","timestamp":1675972420000},"page":"504-513","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status"],"prefix":"10.1002","volume":"169","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3138-7469","authenticated-orcid":false,"given":"Tyler J.","family":"Gathman","sequence":"first","affiliation":[{"name":"School of Medicine University of Minnesota  Minnesota Minneapolis USA"},{"name":"Department of Biomedical Engineering University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Janet S.","family":"Choi","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ranveer M.S.","family":"Vasdev","sequence":"additional","affiliation":[{"name":"School of Medicine University of Minnesota  Minnesota Minneapolis USA"},{"name":"Department of Biomedical Engineering University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamee A.","family":"Schoephoerster","sequence":"additional","affiliation":[{"name":"School of Medicine University of Minnesota  Minnesota Minneapolis USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meredith E.","family":"Adams","sequence":"additional","affiliation":[{"name":"Department of Otolaryngology University of Minnesota  Minneapolis Minnesota USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,2,9]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2016.3527"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1093\/geront\/gnz009"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2017.2513"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaoto.2020.0674"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12877-021-02274-y"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/archinte.168.14.1522"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/0003489416649972"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2020.24855"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2021.2566"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.3766\/jaaa.19.2.7"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1044\/1059-0889(2008\/002)"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1177\/014556130508400111"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.3122\/jabfm.2016.03.150235"},{"key":"e_1_2_8_15_1","doi-asserted-by":"crossref","unstructured":"Committee on Accessible and Affordable Hearing Health Care for Adults Board on Health Sciences Policy Health and Medicine Division National Academies of Sciences Engineering and Medicine BlazerDG DomnitzS LivermanCT eds.Hearing Health Care for Adults: Priorities for Improving Access and Affordability.\u00a0National Academies Press.\u00a02016:23446.doi:10.17226\/23446","DOI":"10.17226\/23446"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2012-002394"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.1988.03720190043029"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1097\/AUD.0000000000000993"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-72685-1"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsa2001090"},{"issue":"1","key":"e_1_2_8_21_1","first-page":"30","article-title":"Intelligent clinical decision support to improve safe opioid management of chronic noncancer pain in primary care","volume":"18","author":"Price\u2010Haywood EG","year":"2018","journal-title":"Ochsner J"},{"key":"e_1_2_8_22_1","unstructured":"Centers for Disease Control and Prevention (CDC) \u00a0National Center for Health Statistics (NCHS). 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