Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning

• URL: http://arxiv.org/abs/2502.06134v1
• Date: Mon, 10 Feb 2025 03:49:41 GMT
• Title: Integrating Sequence and Image Modeling in Irregular Medical Time Series Through Self-Supervised Learning
• Authors: Liuqing Chen, Shuhong Xiao, Shixian Ding, Shanhai Hu, Lingyun Sun,
• Abstract summary: We propose a joint learning framework that incorporates both sequence and image representations. Our approach outperforms seven other state-of-the-art models in three representative real-world clinical datasets.
• Score: 10.94618770233344
• License:
• Abstract: Medical time series are often irregular and face significant missingness, posing challenges for data analysis and clinical decision-making. Existing methods typically adopt a single modeling perspective, either treating series data as sequences or transforming them into image representations for further classification. In this paper, we propose a joint learning framework that incorporates both sequence and image representations. We also design three self-supervised learning strategies to facilitate the fusion of sequence and image representations, capturing a more generalizable joint representation. The results indicate that our approach outperforms seven other state-of-the-art models in three representative real-world clinical datasets. We further validate our approach by simulating two major types of real-world missingness through leave-sensors-out and leave-samples-out techniques. The results demonstrate that our approach is more robust and significantly surpasses other baselines in terms of classification performance.

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