Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.
Nursing Department, Eye & ENT Hospital, Fudan University, Shanghai, China.
J Med Internet Res. 2022 Aug 3;24(8):e34126. doi: 10.2196/34126.
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.
This study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo.
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.
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.
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.
过去 20 年来，问卷调查已被用于预测眩晕诊断并辅助临床决策。基于问卷的机器学习模型有望提高前庭疾病诊断的效率。
本研究旨在开发和验证一种基于问卷的机器学习模型，以预测眩晕的诊断。
在这项多中心前瞻性研究中，2019 年 8 月至 2021 年 3 月，7 家三级转诊中心的耳鼻喉科和眩晕诊所连续纳入就诊时表现为眩晕的患者，随访期为 2 个月。所有参与者在合格筛选后均完成诊断问卷。仅由治疗专家对其主要主诉做出 1 项最终诊断的患者被纳入模型开发和验证。2021 年 2 月 1 日前入组的患者数据用于建模和交叉验证，此后入组的患者进入外部验证。
共纳入 1693 例患者，应答率为 96.2%（1693/1760）。中位年龄为 51（IQR 38-61）岁，991 例（58.5%）为女性；1041 例（61.5%）患者在研究期间获得最终诊断。其中，928 例（54.8%）患者被纳入模型开发和验证，此后入组的 113 例（6.7%）患者被用作外部验证的测试集。这些患者被分为 5 种诊断类别。我们比较了 9 种候选机器学习方法，轻梯度提升机的重新校准模型在交叉验证中的曲线下面积为 0.937（95%CI 0.917-0.962），外部验证中的曲线下面积为 0.954（95%CI 0.944-0.967），表现最佳。
基于问卷的轻梯度提升机能预测常见的前庭疾病，并辅助耳鼻喉科和眩晕诊所的决策。进一步的研究需要更大的样本量和神经科医生的参与，以评估这种机器学习方法的泛化性和稳健性。
