Tensor Coupled Learning of Incomplete Longitudinal Features and Labels for Clinical Score Regression.

Qing Xiao, Guiying Liu, Qianjin Feng, Yu Zhang, Zhenyuan Ning
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Abstract

Longitudinal data with incomplete entries pose a significant challenge for clinical score regression over multiple time points. Although many methods primarily estimate longitudinal scores with complete baseline features (i.e., features collected at the initial time point), such snapshot features may overlook beneficial latent longitudinal traits for generalization. Alternatively, certain completion approaches (e.g., tensor decomposition technology) have been proposed to impute incomplete longitudinal data before score estimation, most of which, however, are transductive and cannot utilize label semantics. This work presents a tensor coupled learning (TCL) paradigm of incomplete longitudinal features and labels for clinical score regression. The TCL enjoys three advantages: 1) It drives semantic-aware factor matrices and collaboratively deals with incomplete longitudinal entries (of features and labels), during which a dynamic regularizer is designed for adaptive attribute selection. 2) It establishes a closed loop connecting baseline features and the coupled factor matrices, which enables inductive inference of longitudinal scores relying on only baseline features. 3) It reinforces the information encoding of baseline data by preserving the local manifold of longitudinal feature space and detecting the temporal alteration across multiple time points. Extensive experiments demonstrate the remarkable performance improvement of our method on clinical score regression with incomplete longitudinal data.

用于临床评分回归的不完整纵向特征和标签的张量耦合学习。
条目不完整的纵向数据给多时间点的临床评分回归带来了巨大挑战。虽然许多方法主要通过完整的基线特征(即在初始时间点收集的特征)来估算纵向评分,但这种快照特征可能会忽略有利于归纳的潜在纵向特征。另外,还有人提出了一些完成方法(如张量分解技术),用于在估算分数之前对不完整的纵向数据进行归因,但其中大多数都是转导式的,无法利用标签语义。本研究提出了一种用于临床评分回归的不完整纵向特征和标签的张量耦合学习(TCL)范式。张量耦合学习有三个优势1) 它能驱动语义感知因子矩阵,协同处理不完整的纵向条目(特征和标签),其间设计了一个动态正则器,用于自适应属性选择。2) 它建立了一个连接基线特征和耦合因子矩阵的闭环,从而能够仅依靠基线特征对纵向分数进行归纳推理。3) 它通过保留纵向特征空间的局部流形和检测多个时间点的时间变化,加强了基线数据的信息编码。广泛的实验证明了我们的方法在不完整纵向数据的临床评分回归中取得了显著的性能改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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