可解释的深度学习，用于使用智能手机远程表征多发性硬化症中的Ambassador
Interpretable Deep Learning for the Remote Characterisation of Ambulation in Multiple Sclerosis using Smartphones
摘要：
智能手机等数字技术在医疗保健应用中的出现表明，有可能开发出丰富、连续和客观的多发性硬化症（MS）残疾指标，这些指标可以远程和门诊外管理。在这项工作中，与标准的支持向量机（SVM）基于特征的方法相比，应用于智能手机惯性传感器数据的深度卷积神经网络（DCNN）可以更好地区分健康与MS参与者的行为。为了克服远程生成的健康数据的典型局限性，如受试者数量少、数据稀疏和异构等，提出了一种基于类似大型开源数据集的迁移学习（TL）模型。我们的TL框架利用了从类似的基于智能手机的传感器数据中收集的人类活动识别（HAR）任务的动态信息。“黑匣子”深度网络缺乏透明度仍然是深度学习在临床应用中被更广泛接受的最大障碍之一。因此，接下来的工作旨在使用分层相关传播（LRP）可视化相关热图归因的DCNN决策。通过LRP框架，可以开始建立和理解从基于智能手机的惯性传感器数据中捕获的模式，这些模式反映了健康人与MS患者（PwMS）的关系。解释表明，基于节奏的措施，步态速度和走动相关的信号扰动是区别MS残疾与健康参与者的独特特征。从高频率的门诊评估中产生的稳健和可解释的结果可以大大增强PwMS目前的门诊评估，为更好的疾病管理技术提供信息，并能够开发更好的治疗干预措施。
英文摘要：
The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. In this work, deep convolutional neural networks (DCNN) applied to smartphone inertial sensor data were shown to better distinguish healthy from MS participant ambulation, compared to standard Support Vector Machine (SVM) feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework utilised the ambulatory information learned on Human Activity Recognition (HAR) tasks collected from similar smartphone-based sensor data. A lack of transparency of "black-box" deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus persons with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.
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