A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study

作者全名："Yu, Fangzhou; Wu, Peixia; Deng, Haowen; Wu, Jingfang; Sun, Shan; Yu, Huiqian; Yang, Jianming; Luo, Xianyang; He, Jing; Ma, Xiulan; Wen, Junxiong; Qiu, Danhong; Nie, Guohui; Liu, Rizhao; Hu, Guohua; Chen, Tao; Zhang, Cheng; Li, Huawei"

作者地址："[Yu, Fangzhou; Wu, Jingfang; Yu, Huiqian; Li, Huawei] Fudan Univ, Eye & ENT Hosp, Dept Otorhinolaryngol, Room 611,Bldg 9,83 Fenyang Rd, Shanghai 200031, Peoples R China; [Wu, Peixia] Fudan Univ, Eye & ENT Hosp, Nursing Dept, Shanghai, Peoples R China; [Deng, Haowen; Zhang, Cheng] Fudan Univ, Dept Informat Management & Informat Syst, Shanghai, Peoples R China; [Wu, Jingfang; Yu, Huiqian; Li, Huawei] Fudan Univ, State Key Lab Med Neurobiol, Frontiers Ctr Brain Sci, Shanghai, Peoples R China; [Wu, Jingfang; Yu, Huiqian; Li, Huawei] Fudan Univ, Minist Educ, Frontiers Ctr Brain Sci, Shanghai, Peoples R China; [Wu, Jingfang; Sun, Shan; Yu, Huiqian; Li, Huawei] Fudan Univ, Natl Hlth Commiss, Key Lab Hearing Med, Shanghai, Peoples R China; [Sun, Shan; Li, Huawei] Fudan Univ, Inst Brain Sci, Shanghai, Peoples R China; [Sun, Shan; Li, Huawei] Fudan Univ, Collaborat Innovat Ctr Brain Sci, Shanghai, Peoples R China; [Yang, Jianming] Anhui Med Univ, Dept Otorhinolaryngol Head & Neck Surg, Affiliated Hosp 2, Hefei, Peoples R China; [Luo, Xianyang; He, Jing] Xiamen Univ, Affiliated Hosp 1, Med Coll, Dept Otolaryngol Head & Neck Surg, Xiamen, Peoples R China; [Ma, Xiulan; Wen, Junxiong] China Med Univ, Dept Otolaryngol Head & Neck Surg, Shengjing Hosp, Shenyang, Peoples R China; [Qiu, Danhong] Shanghai Pudong Hosp, Dept Otolaryngol, Shanghai, Peoples R China; [Nie, Guohui; Liu, Rizhao] Shenzhen Second Peoples Hosp, Dept Otolaryngol, Shenzhen, Peoples R China; [Hu, Guohua; Chen, Tao] Chongqing Med Univ, Dept Otolaryngol, Affiliated Hosp 1, Chongqing, Peoples R China; [Li, Huawei] Fudan Univ, Inst Biomed Sci, Shanghai, Peoples R China"

通信作者："Li, HW (通讯作者)，Fudan Univ, Eye & ENT Hosp, Dept Otorhinolaryngol, Room 611,Bldg 9,83 Fenyang Rd, Shanghai 200031, Peoples R China."

来源：JOURNAL OF MEDICAL INTERNET RESEARCH

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000862701700009

JCR分区：Q1

影响因子：7.4

年份：2022

卷号：24

期号：8

开始页：　

结束页：　

文献类型：Article

关键词：vestibular disorders; machine learning; diagnostic model; vertigo; ENT; questionnaire

摘要："Background: Questionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders. Objective: This study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo. Methods: In this multicenter prospective study, patients presenting with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation. Results: A total of 1693 patients were enrolled, with a response rate of 96.2% (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5%) females; 1041 (61.5%) patients received the final diagnosis during the study period. Among them, 928 (54.8%) patients were included in model development and validation, and 113 (6.7%) patients who enrolled later were used as a test set for external validation. They were classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% CI 0.917-0.962) in cross-validation and 0.954 (95% CI 0.944-0.967) in external validation. Conclusions: The questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision-making in ENT and vertigo clinics. Further studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method."

基金机构："Capacity Building Project for interdisciplinary diagnosis and treatment of major diseases (otogenic vertigo); Shanghai Municipal Health Commission; General Project of Scientific Research Fund of Shanghai Health Committee [202040286]; National Natural Science Foundation of China [91846302, 72033003]; Shanghai Municipal Key Clinical Specialty [shslczdzk00801]"

基金资助正文："This study was supported by the Capacity Building Project for interdisciplinary diagnosis and treatment of major diseases (otogenic vertigo) , Shanghai Municipal Health Commission, Shanghai Municipal Key Clinical Specialty (shslczdzk00801) , General Project of Scientific Research Fund of Shanghai Health Committee (grant 202040286) , and the National Natural Science Foundation of China (91846302 and 72033003) . The funding sources played no part in the design and conduct of this study; collection, management, analysis, and interpretation of data; writing of the report; or the decision to submit the manuscript for publication."