一个用于预测助听器满意度的可解释机器学习框架：整合HATASS工具与临床见解

文献信息

中文摘要

背景： 助听器技术适应和用户满意度受多种相互作用的人口学、临床和行为因素影响，仅靠传统统计方法难以可靠预测结局。本研究提出一个可解释的机器学习框架，通过整合人口学特征、助听器相关变量以及来自助听器技术适应与满意度量表（HATASS）的患者报告结局，来探究助听器满意度的多维决定因素。方法：采用五折交叉验证策略，对五种回归算法——线性回归、决策树回归（DTR）、随机森林回归（RFR）、支持向量回归和梯度提升回归（GBR）——进行比较评估。使用均方根误差（RMSE）、平均绝对误差（MAE）和决定系数（R2）评估预测性能，同时通过交叉验证的袋外置换特征重要性分析考察模型可解释性。结果：在所评估的算法中，随机森林回归在所采用的验证框架下取得了最一致的预测性能，产生最低的平均RMSE（14.225）和最高的平均R2（0.146）。尽管总体预测性能仍然有限，但可解释性分析一致地将年龄确定为最具影响力的预测因子，其次是发病年份、教育水平、助听器使用时长和每日助听器使用。相比之下，性别、电池类型、耳鸣和眩晕对模型预测的贡献相对较小。结论：这些发现表明，助听器适应和满意度源于人口学、行为、临床和设备相关特征之间复杂的非线性相互作用，而非孤立的线性关联。所提出的框架为研究助听器技术适应和满意度提供了一种可解释、经内部验证的分析方法，并为未来整合全面听力学测量、纵向随访数据和独立外部验证的研究奠定了基础，以支持开发更透明和个性化的听力医疗系统。

英文摘要

Hearing aid technology adaptation and user satisfaction are influenced by multiple interacting demographic, clinical, and behavioral factors, making reliable prediction of outcomes challenging with conventional statistical approaches alone. This study proposes an explainable machine learning framework to investigate the multidimensional determinants of hearing aid satisfaction by integrating demographic characteristics, hearing aid-related variables, and patient-reported outcomes obtained from the Hearing Aid Technology Adaptation and Satisfaction Scale (HATASS). Five regression algorithms-Linear Regression, Decision Tree Regression (DTR), Random Forest Regression (RFR), Support Vector Regression, and Gradient Boosting Regression (GBR)-were comparatively evaluated using a five-fold cross-validation strategy. Predictive performance was assessed using the root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2), while model interpretability was investigated through cross-validated out-of-bag permutation feature importance analysis. Among the evaluated algorithms, Random Forest Regression achieved the most consistent predictive performance, yielding the lowest average RMSE (14.225) and the highest average R2 (0.146) under the adopted validation framework. Although the overall predictive performance remained modest, the explainability analysis consistently identified age as the most influential predictor, followed by onset year, education level, hearing aid usage duration, and daily hearing aid use. In contrast, gender, battery type, tinnitus, and vertigo contributed comparatively less to model predictions. These findings indicate that hearing aid adaptation and satisfaction arise from complex nonlinear interactions among demographic, behavioral, clinical, and device-related characteristics rather than isolated linear associations. The proposed framework provides an interpretable, internally validated analytical approach for investigating hearing aid technology adaptation and satisfaction and establishes a foundation for future studies that integrate comprehensive audiological measurements, longitudinal follow-up data, and independent external validation to support the development of more transparent and personalized hearing healthcare systems.