Related papers: Data-Driven Discovery of Feature Groups in Clinical Time Series

Data-Driven Discovery of Feature Groups in Clinical Time Series

URL: http://arxiv.org/abs/2511.08260v1
Date: Wed, 12 Nov 2025 01:49:30 GMT
Title: Data-Driven Discovery of Feature Groups in Clinical Time Series
Authors: Fedor Sergeev, Manuel Burger, Polina Leshetkina, Vincent Fortuin, Gunnar Rätsch, Rita Kuznetsova,
Abstract summary: Grouping of features based on similarity and relevance to a prediction task has been shown to enhance the performance of deep learning architectures.<n>We propose a novel method that learns feature groups by clustering weights of feature-wise embedding layers.<n>We demonstrate that our method outperforms static clustering approaches on synthetic data and achieves performance comparable to expert-defined groups on real-world medical data.
Score: 11.418915308804822
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Clinical time series data are critical for patient monitoring and predictive modeling. These time series are typically multivariate and often comprise hundreds of heterogeneous features from different data sources. The grouping of features based on similarity and relevance to the prediction task has been shown to enhance the performance of deep learning architectures. However, defining these groups a priori using only semantic knowledge is challenging, even for domain experts. To address this, we propose a novel method that learns feature groups by clustering weights of feature-wise embedding layers. This approach seamlessly integrates into standard supervised training and discovers the groups that directly improve downstream performance on clinically relevant tasks. We demonstrate that our method outperforms static clustering approaches on synthetic data and achieves performance comparable to expert-defined groups on real-world medical data. Moreover, the learned feature groups are clinically interpretable, enabling data-driven discovery of task-relevant relationships between variables.

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