Self-supervised learning approaches provide a promising direction for
clustering multivariate time-series data. However, real-world time-series data
often include missing values, and the existing approaches require imputing
missing values before clustering, which may cause extensive computations and
noise and result in invalid interpretations. To address these challenges, we
present a Self-supervised Learning-based Approach to Clustering multivariate
Time-series data with missing values (SLAC-Time). SLAC-Time is a
Transformer-based clustering method that uses time-series forecasting as a
proxy task for leveraging unlabeled data and learning more robust time-series
representations. This method jointly learns the neural network parameters and
the cluster assignments of the learned representations. It iteratively clusters
the learned representations with the K-means method and then utilizes the
subsequent cluster assignments as pseudo-labels to update the model parameters.
To evaluate our proposed approach, we applied it to clustering and phenotyping
Traumatic Brain Injury (TBI) patients in the TRACK-TBI dataset. Our experiments
demonstrate that SLAC-Time outperforms the baseline K-means clustering
algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn
index, and Davies Bouldin index. We identified three TBI phenotypes that are
distinct from one another in terms of clinically significant variables as well
as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE)
score, Intensive Care Unit (ICU) length of stay, and mortality rate. The
experiments show that the TBI phenotypes identified by SLAC-Time can be
potentially used for developing targeted clinical trials and therapeutic
strategies.

提供了一种名为 SLAC-Time 的自监督学习基于 Transformer 聚类多元时间序列数据的方法，该方法通过利用时间序列预测作为代理任务来学习更健壮的时间序列表示，并使用 K-means 进行聚类，实验表明，该方法在聚类 TBI 患者中表现优异，可以用于开发有针对性的临床试验和治疗策略。

一种基于自监督学习的方法用于聚类多变量时间序列数据中的缺失值 (SLAC-Time): 一种用于创伤性脑损伤表型化的应用

A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to Traumatic Brain Injury Phenotyping