Learning invariant representation from high- dimensional data for quantitative stroke reha

从高维数据中学习不变表示以进行定量中风康复

基本信息

批准号：
10469389
负责人：
Carlos Fernandez-Granda
金额：
$ 30万
依托单位：
NEW YORK UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-16 至 2023-06-30
项目状态：
已结题

项目摘要

Advances in wearable electronics, personal mobile devices, and sensor technology are opening the door to many promising applications in medical care and biomedical research. However, the resulting datasets are often challenging to process due to variability caused by extraneous effects unrelated to the tasks of interest, such as changes in environmental conditions, heteroscedasticity in measurement noise, or patient idiosyncrasies. These effects produce systematic differences between the data used to train machine- learning algorithms and the data on which they are applied in practice, impairing real-world performance. The proposed research will address the fundamental problem of factoring out extraneous effects associated with known nuisance variables. We will develop a novel methodology for extracting features that ar.e invariant to nuisance variables-and hence also to the associated extraneous effects-but that are still useful for classification or regression. The methodology is based on nonparametric deep-network models that perform automatic normalization of the data, and further enforce invariance via adversarial learning. We will apply the approach to an important problem in stroke rehabilitation, the quantitated dosing of motor training. Using a dataset of sensor-based motion data, we will train the model to identify and count functional movements in stroke patients performing rehabilitation activities. We expect to show that our approach can surmount patient variability to enable rigorous movement classification and quantitation. The proposed work is significant, because it will empower investigators to undertake the dosing trials critically needed in stroke rehabilitation. The proposed work is innovative, because it departs from traditional data preprocessing techniques by combining advanced data normalization and model calibration procedures. Our work is likely to have a positive impact on stroke rehabilitation by facilitating the research required to change clinical practice and improve stroke outcomes. Our quantitative approach is broadly generalizable to applications hindered by nuisance variables, such as medical diagnostics and genomics.

可穿戴电子产品、个人移动设备和传感器技术的进步为在医疗保健和生物医学研究中有许多有前景的应用。然而，得到的数据集是由于与任务无关的外部影响所引起的可变性，通常对流程具有挑战性兴趣，例如环境条件的变化、测量噪声的异方差性或患者特质。这些影响会产生用于训练机器的数据之间的系统差异学习算法及其在实践中应用的数据，从而损害现实世界的性能。拟议的研究将解决排除无关影响的基本问题与已知的有害变量相关。我们将开发一种新颖的方法来提取特征对于有害变量是不变的，因此对于相关的无关效应也是不变的，但仍然是对于分类或回归很有用。该方法基于非参数深度网络模型执行数据的自动标准化，并通过对抗性学习进一步增强不变性。我们将将该方法应用于中风康复中的一个重要问题，即运动神经元的定量给药训练。使用基于传感器的运动数据集，我们将训练模型进行识别和计数中风患者进行康复活动的功能性运动。我们希望表明我们的该方法可以克服患者的变异性，从而实现严格的运动分类和定量。这拟议的工作意义重大，因为它将使研究人员能够批判性地进行剂量试验中风康复所需。拟议的工作具有创新性，因为它脱离了传统数据结合先进的数据标准化和模型校准程序的预处理技术。我们的工作可能会通过促进中风康复所需的研究对中风康复产生积极影响。改变临床实践并改善卒中结果。我们的定量方法具有广泛的普适性受干扰变量阻碍的应用，例如医疗诊断和基因组学。