Saak Samira K, Hildebrandt Andrea, Kollmeier Birger, Buhl Mareike
Department of Psychology, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Cluster of Excellence Hearing4all, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany.
Front Digit Health. 2020 Dec 15;2:596433. doi: 10.3389/fdgth.2020.596433. eCollection 2020.
The application of machine learning for the development of clinical decision-support systems in audiology provides the potential to improve the objectivity and precision of clinical experts' diagnostic decisions. However, for successful clinical application, such a tool needs to be accurate, as well as accepted and trusted by physicians. In the field of audiology, large amounts of patients' data are being measured, but these are distributed over local clinical databases and are heterogeneous with respect to the applied assessment tools. For the purpose of integrating across different databases, the Common Audiological Functional Parameters (CAFPAs) were recently established as abstract representations of the contained audiological information describing relevant functional aspects of the human auditory system. As an intermediate layer in a clinical decision-support system for audiology, the CAFPAs aim at maintaining interpretability to the potential users. Thus far, the CAFPAs were derived by experts from audiological measures. For designing a clinical decision-support system, in a next step the CAFPAs need to be automatically derived from available data of individual patients. Therefore, the present study aims at predicting the expert generated CAFPA labels using three different machine learning models, namely the lasso regression, elastic nets, and random forests. Furthermore, the importance of different audiological measures for the prediction of specific CAFPAs is examined and interpreted. The trained models are then used to predict CAFPAs for unlabeled data not seen by experts. Prediction of unlabeled cases is evaluated by means of model-based clustering methods. Results indicate an adequate prediction of the ten distinct CAFPAs. All models perform comparably and turn out to be suitable choices for the prediction of CAFPAs. They also generalize well to unlabeled data. Additionally, the extracted relevant features are plausible for the respective CAFPAs, facilitating interpretability of the predictions. Based on the trained models, a prototype of a clinical decision-support system in audiology can be implemented and extended towards clinical databases in the future.
机器学习在听力学临床决策支持系统开发中的应用，为提高临床专家诊断决策的客观性和精确性提供了潜力。然而，为了成功应用于临床，这样的工具不仅要准确，还需要得到医生的认可和信任。在听力学领域，正在测量大量患者数据，但这些数据分布在本地临床数据库中，并且在所应用的评估工具方面存在异质性。为了跨不同数据库进行整合，最近建立了通用听力学功能参数（CAFPAs），作为所包含的听力学信息的抽象表示，描述了人类听觉系统的相关功能方面。作为听力学临床决策支持系统的中间层，CAFPAs旨在保持对潜在用户的可解释性。到目前为止，CAFPAs是由听力学测量专家推导出来的。为了设计临床决策支持系统，下一步需要从个体患者的可用数据中自动推导CAFPAs。因此，本研究旨在使用三种不同的机器学习模型，即套索回归、弹性网络和随机森林，预测专家生成的CAFPA标签。此外，还对不同听力学测量对特定CAFPAs预测的重要性进行了检验和解释。然后，使用训练好的模型对专家未见过的未标记数据预测CAFPAs。通过基于模型的聚类方法评估未标记病例的预测。结果表明对十个不同的CAFPAs有充分的预测。所有模型表现相当，都是预测CAFPAs的合适选择。它们对未标记数据也有很好的泛化能力。此外，提取的相关特征对于各自的CAFPAs是合理的，便于预测的可解释性。基于训练好的模型，未来可以实现听力学临床决策支持系统的原型，并向临床数据库扩展。
