Learning semantic-rich representations from raw unlabeled time series data is critical for downstream tasks such as classification and forecasting. Contrastive learning has recently shown its promising representation learning capability in the absence of expert annotations. However, existing contrastive approaches generally treat each instance independently, which leads to false negative pairs that share the same semantics. To tackle this problem, we propose MHCCL, a Masked Hierarchical Cluster-wise Contrastive Learning model, which exploits semantic information obtained from the hierarchical structure consisting of multiple latent partitions for multivariate time series. Motivated by the observation that fine-grained clustering preserves higher purity while coarse-grained one reflects higher-level semantics, we propose a novel downward masking strategy to filter out fake negatives and supplement positives by incorporating the multi-granularity information from the clustering hierarchy. In addition, a novel upward masking strategy is designed in MHCCL to remove outliers of clusters at each partition to refine prototypes, which helps speed up the hierarchical clustering process and improves the clustering quality. We conduct experimental evaluations on seven widely-used multivariate time series datasets. The results demonstrate the superiority of MHCCL over the state-of-the-art approaches for unsupervised time series representation learning.

The rapidly increasing adoption of Electronic Health Records (EHRs) has provided us with a comprehensive and reliable data repository, enabling the robust support for clinical decision making and the efficacious management of cardiovascular diseases. Patients may receive various treatment regimens due to the idiosyncratic dissimilarities in physical conditions. Hence, assigning specific category labels to treatment regimens for individual patients poses a challenge owing to the intricate nature of diverse medications and patient profiles. In this paper, we address this challenge by developing a novel model for Treatment Regimen Recommendations based on Siamese Neural Networks (TRRSNN). TRRSNN is harnessed to learn a global similarity metric from EHRs, facilitating the comparison and matching of target patients from unknown categories. The objective is to proffer recommendations of pre-existing treatments of patients that exhibit the highest semblance to those of target patients. Our proposed TRRSNN obviates the necessity for categorically labeling treatment regimens, and instead focuses on discerning the similarity among treatment regimens for pairwise patients. We conduct empirical evaluations on two real-world datasets, and the results demonstrate the superiority of our proposed TRRSNN over the baselines for treatment regimen recommendations.

Hospital readmission prediction employs reliable predictive models to evaluate the readmission risk of patients upon discharge. Identifying patients with high readmission risk and paying additional attention to them can ease the burden on both patients and society. Recently, considerable attention has been paid to personalized readmission predictions, i.e., building an independent model for each target patient. However, existing personalized predictive models can be easily affected by data insufficiency and provide poor generalization capabilities. To address these challenges, in this paper, we propose the CLusterbased mUlti-task lEarning model (CLUE) to achieve personalized hospital readmission prediction. CLUE groups patients into different clusters based on a multi-angle similarity metric to preserve the interrelated information of patients with highly similar clinical behaviors. Due to different group characteristics of patients, the clusters of patients are imbalanced. Given that, CLUE treats the hospital readmission prediction for each cluster of patients as one task and learns multiple tasks jointly by parameter sharing mechanisms. In this way, not only can the data insufficiency problem be alleviated by supplementing individual models with the shared information from other clusters, but also the specific information of each cluster can be preserved for personalization. We conduct extensive experiments on a real-world dataset of electronic health records, and show that CLUE significantly outperforms competitive comparative methods.