背景:隐性GJB2变体,听力损失最常见的遗传原因,可能导致进行性感音神经性听力损失(SNHL)。这项研究的目的是使用机器学习为GJB2相关的SNHL建立一个现实的预测模型,以实现及时干预的个性化医疗计划。
方法:在2005年至2022年的全国队列中,纳入了具有双等位基因GJB2变异的SNHL患者。将不同的数据预处理协议和计算算法结合起来构建预测模型。我们将数据集随机分成训练,验证,和测试集以72:8:20的比例,并重复此过程十次以获得平均结果。使用平均绝对误差(MAE)评估模型的性能,这是指预测和实际听力阈值之间的差异。
结果:我们招募了449名患者,其中2184个听力图可用于深度学习分析。SNHL进展在所有模型中都被确定,并且与年龄无关,性别,和基因型。平均听力进展率为每年0.61dBHL。线性回归的最佳MAE,多层感知器,长期短期记忆,注意模型分别为4.42、4.38、4.34和4.76dBHL,分别。长短期记忆模型表现最好,平均MAE为4.34dBHL,可接受的精度长达4年。
结论:我们开发了一种预后模型,该模型使用机器学习来近似GJB2相关SNHL的真实听力进展,允许设计个性化的医疗计划,例如推荐该人群的最佳随访间隔。
BACKGROUND: Recessive
GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for
GJB2-related SNHL using machine learning to enable personalized medical planning for timely intervention.
METHODS: Patients with SNHL with confirmed biallelic
GJB2 variants in a nationwide cohort between 2005 and 2022 were included. Different data preprocessing protocols and computational algorithms were combined to construct a prediction model. We randomly divided the dataset into training, validation, and test sets at a ratio of 72:8:20, and repeated this process ten times to obtain an average result. The performance of the models was evaluated using the mean absolute error (MAE), which refers to the discrepancy between the predicted and actual hearing thresholds.
RESULTS: We enrolled 449 patients with 2184 audiograms available for deep learning analysis. SNHL progression was identified in all models and was independent of age, sex, and genotype. The average hearing progression rate was 0.61 dB HL per year. The best MAE for linear regression, multilayer perceptron, long short-term memory, and attention model were 4.42, 4.38, 4.34, and 4.76 dB HL, respectively. The long short-term memory model performed best with an average MAE of 4.34 dB HL and acceptable accuracy for up to 4 years.
CONCLUSIONS: We have developed a prognostic model that uses machine learning to approximate realistic hearing progression in
GJB2-related SNHL, allowing for the design of individualized medical plans, such as recommending the optimal follow-up interval for this population.