Machine Learning Prediction of Objective Hearing Loss With Demographics, Clinical Factors, and Subjective Hearing Status

医学 接收机工作特性 置信区间 听力损失 听力学 全国健康与营养检查调查 人口统计学的 队列 逐步回归 逻辑回归 助听器 体质指数 人口学 内科学 人口 社会学 环境卫生
作者
Tyler J. Gathman,Janet S. Choi,Ranveer Vasdev,Jamee Schoephoerster,Meredith E. Adams
出处
期刊:Otolaryngology-Head and Neck Surgery [Wiley]
卷期号:169 (3): 504-513 被引量:6
标识
DOI:10.1002/ohn.288
摘要

Abstract Objective 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‐frequency pure‐tone average (PTA). Study Design Cross‐sectional study. Setting National Health and Nutrition Examination Survey (NHANES). Methods The cohort included 8918 adults (≥20 years) who completed audiometric testing with NHANES (2012‐2018). The primary outcome measure was the prediction of better hearing ear speech‐frequency PTA. Relevant predictors included demographics, medical conditions, and subjective assessment of hearing. Supervised ML with a tree‐based 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). Results Using the full set of predictors, the test set MAE between the ML‐predicted and actual PTA was 5.29 dB HL (95% confidence interval [CI]: 4.97‐5.61). 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‐factor abbreviated model performed comparably to the extended feature set with MAE 5.36 (95% CI: 5.03‐5.69) and AUC for PTA > 25 dB HL of 0.92 (95% CI: 0.90‐0.94). Conclusion The ML model was able to predict PTA with patient demographics, clinical factors, and subjective hearing status. ML‐based prediction may be used to identify individuals who could benefit most from audiometric evaluation.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
酷波er应助熊啊采纳,获得10
2秒前
cc完成签到,获得积分10
8秒前
青塘龙仔发布了新的文献求助10
8秒前
Lucas应助青果采纳,获得10
9秒前
务实玫瑰完成签到,获得积分10
11秒前
谢謝发布了新的文献求助10
13秒前
jiangjiang完成签到 ,获得积分10
14秒前
15秒前
lcx完成签到,获得积分10
15秒前
16秒前
上官若男应助嘟嘟豆806采纳,获得30
18秒前
烧麦专家发布了新的文献求助10
19秒前
lmm完成签到,获得积分10
20秒前
青果发布了新的文献求助10
21秒前
purple完成签到,获得积分10
25秒前
震动的白山完成签到 ,获得积分10
26秒前
27秒前
熊啊发布了新的文献求助10
31秒前
33秒前
星星会开花完成签到,获得积分10
33秒前
宁宁完成签到 ,获得积分10
34秒前
CCC完成签到,获得积分10
34秒前
流流124141完成签到,获得积分10
34秒前
爆米花应助熊啊采纳,获得10
35秒前
科研通AI5应助归雁采纳,获得30
35秒前
zho发布了新的文献求助10
36秒前
Lq发布了新的文献求助10
37秒前
37秒前
38秒前
小圆圈发布了新的文献求助10
42秒前
科研通AI2S应助lemon采纳,获得10
43秒前
北地风情完成签到 ,获得积分10
43秒前
黑糖珍珠完成签到 ,获得积分10
43秒前
45秒前
科研通AI5应助幸福幻灵采纳,获得10
48秒前
dingz完成签到,获得积分10
48秒前
学术小王子完成签到,获得积分10
50秒前
房房不慌完成签到 ,获得积分10
50秒前
窦逗豆完成签到,获得积分10
51秒前
高分求助中
【此为提示信息,请勿应助】请按要求发布求助,避免被关 20000
Production Logging: Theoretical and Interpretive Elements 3000
CRC Handbook of Chemistry and Physics 104th edition 1000
Izeltabart tapatansine - AdisInsight 600
Introduction to Comparative Public Administration Administrative Systems and Reforms in Europe, Third Edition 3rd edition 500
Distinct Aggregation Behaviors and Rheological Responses of Two Terminally Functionalized Polyisoprenes with Different Quadruple Hydrogen Bonding Motifs 450
THE STRUCTURES OF 'SHR' AND 'YOU' IN MANDARIN CHINESE 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3761824
求助须知:如何正确求助?哪些是违规求助? 3305615
关于积分的说明 10134845
捐赠科研通 3019634
什么是DOI,文献DOI怎么找? 1658255
邀请新用户注册赠送积分活动 792029
科研通“疑难数据库(出版商)”最低求助积分说明 754751