Medical questionnaires are a valuable source of information but are often difficult to analyse due to both their size and the high possibility of them having missing values. This is a problematic issue in biomedical data science as it may complicate how individual questionnaire data is represented for statistical or machine learning analysis. In this paper, we propose a deeply-learnt residual autoencoder to simultaneously perform non-linear data imputation and dimensionality reduction. We present an extensive analysis of the dynamics of the performance of this autoencoder regarding the compression rate and the proportion of missing values. This method is evaluated on motor and non-motor clinical questionnaires of the Parkinson's Progression Markers Initiative (PPMI) database and consistently outperforms linear coupled imputation and reduction approaches.

医疗问卷是一种有价值的信息来源，但由于其大小和缺失值的可能性很高，通常难以分析。这是生物医学数据科学中的一个有问题的问题，因为它可能会使个人问卷数据在统计或机器学习分析中的表示方式复杂化。在本文中，我们提出了一种深度学习的残差自动编码器来同时执行非线性数据插补和降维。我们对该自动编码器的性能动态进行了广泛的分析，涉及压缩率和缺失值的比例。该方法在帕金森氏症的运动和非运动临床问卷上进行了评估