论文标题
通过多模式不规则收集的纵向智能手机数据来预测帕金森氏病
Predicting Parkinson's Disease with Multimodal Irregularly Collected Longitudinal Smartphone Data
论文作者
论文摘要
帕金森病是一种神经疾病，老年人普遍存在。诊断疾病的传统方法依赖于一套活动测试质量的面对面的主观临床评估。如今，智能手机应用程序收集的高分辨率纵向活动数据使得进行远程和方便的健康评估成为可能。然而，外表面测试通常会遭受质量较差的控制和不规则收集的观察结果，从而导致嘈杂的测试结果。为了解决这些问题，我们提出了一种新型基于时间序列的方法，用于通过智能手机在野外收集的原始活动测试数据来预测帕金森氏病。提出的方法首先将离散活动测试同步到统一时间点的多模式特征。接下来，它通过两个注意模块从模态和时间观察中提炼并丰富了局部和全局的表示。通过提出的机制，我们的模型能够处理嘈杂的观测值，同时提取精致的时间特征以改善预测性能。大型公共数据集中的定量和定性结果证明了拟议方法的有效性。
Parkinsons Disease is a neurological disorder and prevalent in elderly people. Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests. The high-resolution longitudinal activity data collected by smartphone applications nowadays make it possible to conduct remote and convenient health assessment. However, out-of-lab tests often suffer from poor quality controls as well as irregularly collected observations, leading to noisy test results. To address these issues, we propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild. The proposed method first synchronizes discrete activity tests into multimodal features at unified time points. Next, it distills and enriches local and global representations from noisy data across modalities and temporal observations by two attention modules. With the proposed mechanisms, our model is capable of handling noisy observations and at the same time extracting refined temporal features for improved prediction performance. Quantitative and qualitative results on a large public dataset demonstrate the effectiveness of the proposed approach.
