Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series (2107.14293v2)

Abstract: Multivariate time-series data are frequently observed in critical care settings and are typically characterized by sparsity (missing information) and irregular time intervals. Existing approaches for learning representations in this domain handle these challenges by either aggregation or imputation of values, which in-turn suppresses the fine-grained information and adds undesirable noise/overhead into the machine learning model. To tackle this problem, we propose a Self-supervised Transformer for Time-Series (STraTS) model which overcomes these pitfalls by treating time-series as a set of observation triplets instead of using the standard dense matrix representation. It employs a novel Continuous Value Embedding technique to encode continuous time and variable values without the need for discretization. It is composed of a Transformer component with multi-head attention layers which enable it to learn contextual triplet embeddings while avoiding the problems of recurrence and vanishing gradients that occur in recurrent architectures. In addition, to tackle the problem of limited availability of labeled data (which is typically observed in many healthcare applications), STraTS utilizes self-supervision by leveraging unlabeled data to learn better representations by using time-series forecasting as an auxiliary proxy task. Experiments on real-world multivariate clinical time-series benchmark datasets demonstrate that STraTS has better prediction performance than state-of-the-art methods for mortality prediction, especially when labeled data is limited. Finally, we also present an interpretable version of STraTS which can identify important measurements in the time-series data. Our data preprocessing and model implementation codes are available at https://github.com/sindhura97/STraTS.

• The paper introduces the STraTS model, which represents clinical time-series data as observation triplets to avoid aggregation and imputation issues.
• It employs Continuous Value Embedding and a Transformer architecture with self-supervised learning to effectively capture temporal dependencies.
• Evaluations on MIMIC-III and PhysioNet-2012 show improved ROC-AUC and PR-AUC metrics, with an interpretable variant enhancing clinical decision insights.

An Evaluation of STraTS: A Self-Supervised Transformer Model for Clinical Time-Series Analysis

The paper under consideration presents the Self-Supervised Transformer for Time-Series (STraTS) model, tailored specifically to address the intricacies of sparse and irregularly sampled multivariate clinical time-series data. Unlike traditional methods that often rely on aggregation or imputation to handle missing values and irregularities, STraTS takes a novel approach by representing time-series as a set of observation triplets. This innovation allows the model to circumvent the noise introduction and overhead associated with conventional data processing techniques, thus preserving the fidelity of the underlying dataset.

Core Innovations in STraTS

STraTS distinguishes itself through several key methodological advancements:

1. Observation Triplet Representation: Instead of framing time-series data in a dense matrix, STraTS handles each series as a collection of observation triplets, consisting of time, variable, and value. This structure eliminates the necessity for aggregation and imputation, thereby retaining fine-grained data characteristics.
2. Continuous Value Embedding (CVE): For the numeric components of data—both time and measurements—STraTS employs a novel CVE scheme. This method uses parameterized feed-forward networks to embed continuous attributes, circumventing the need for discretization and thus maintaining the granularity of temporal data transitions.
3. Transformer Architecture with Self-Supervised Learning: Leveraging the Transformer framework with multi-head attention allows STraTS to construct contextual embeddings of observation triplets, thereby capturing temporal dependencies effectively. Furthermore, to address the limited availability of labeled data in healthcare contexts, STraTS employs self-supervised learning by employing a forecasting auxiliary task during pretraining, substantially enhancing its generalizability.

Comparative Evaluation and Performance

The efficacy of STraTS is validated against established baseline models, such as GRU-D and InterpNet, using two real-world datasets: MIMIC-III and PhysioNet-2012. The results demonstrate that STraTS consistently outperforms these models, particularly in scenarios with limited labeled data, thus highlighting the advantages of its architectural innovations and the inclusion of self-supervised learning.

Empirical assessments reveal that STraTS achieves superior performance in terms of ROC-AUC and PR-AUC metrics, obtaining an impressive PR-AUC of 0.577 and 0.498 on the MIMIC-III and PhysioNet-2012 datasets respectively. These results underscore the model's robustness and prediction accuracy when confronted with the challenges posed by sparse and irregular clinical datasets.

Interpretability and Practical Implications

In recognition of the importance of model transparency in healthcare applications, the paper also introduces an interpretable variant of the model, I-STraTS. This interpretable model version delineates its predictions in terms of individual feature contributions, which could assist practitioners in understanding the model’s decision-making process, thereby fostering trust and potentially guiding clinical decisions.

Future Directions

Future research could explore additional dimensions within STraTS, such as integrating further self-supervised learning tasks to bolster model adaptability to diverse time-series datasets or optimizing computational efficiency for handling lengthier sequences. Additionally, investigating STraTS’ applicability beyond mortality prediction, encompassing other clinical outcomes or domains, could extend its utility.

Overall, the introduction of STraTS marks a significant contribution towards enhancing the analysis of complex clinical time-series, offering a robust framework that combines representational precision with practical utility. Its deployment can support improved patient outcomes through enhanced predictive accuracy and interpretability in critical healthcare settings.