@文章{信息:doi/10.2196/34126，作者="于、方舟与吴、邓佩霞与邓浩文、吴景芳与孙、山与余、慧倩与杨、建明与罗、贤阳与何、静与马、秀兰与文、君雄与邱、丹红与聂、国辉与刘、日照与胡、陈国华与陈、陶与张、程与李、Huawei"，标题="基于问卷的集成学习模型预测眩晕诊断:模型开发与验证研究”，期刊=“J Med Internet Res”，年=“2022”，月=“8”，日=“3”，卷=“24”，数=“8”，页=“e34126”，关键词=“前庭神经障碍;机器学习;诊断模型;眩晕;ENT;背景:在过去的20年里，问卷调查一直被用于预测眩晕的诊断，并协助临床决策。基于问卷的机器学习模型有望提高前庭疾病的诊断效率。目的:本研究旨在开发和验证一个基于问卷的机器学习模型，预测眩晕的诊断。方法:在这项多中心前瞻性研究中，2019年8月至2021年3月，眩晕患者在首次访问7个三级转诊中心的耳鼻喉科和眩晕科诊所时进入连续队列，随访期为2个月。 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. ", issn="1438-8871", doi="10.2196/34126", url="//www.mybigtv.com/2022/8/e34126", url="https://doi.org/10.2196/34126", url="http://www.ncbi.nlm.nih.gov/pubmed/35921135" }