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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Learning to Exploit Invariances in Clinical Time-Series Data using Sequence Transformer Networks.

Jeeheh Oh1, Jiaxuan Wang1, Jenna Wiens1

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI.

Proceedings of Machine Learning Research
|July 24, 2025
PubMed
Summary
This summary is machine-generated.

Sequence Transformer Networks learn invariances in clinical time-series data, outperforming convolutional neural networks (CNNs) for predicting in-hospital mortality. This approach directly learns data patterns for improved predictive accuracy.

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Area of Science:

  • Machine Learning in Healthcare
  • Clinical Time-Series Analysis
  • Artificial Intelligence in Medicine

Background:

  • Convolutional Neural Networks (CNNs) are increasingly used for clinical time-series data due to computational efficiency and ability to exploit temporal invariances.
  • Clinical data often exhibits various invariances (e.g., scaling) beyond phase invariance, which standard CNNs may not fully capture.
  • Existing preprocessing methods like dynamic time warping require prior identification of invariance types.

Purpose of the Study:

  • To introduce Sequence Transformer Networks, an end-to-end trainable architecture for clinical time-series data.
  • To enable models to automatically identify and account for diverse invariances within the data.
  • To improve the prediction of in-hospital mortality using learned invariances.

Main Methods:

  • Proposed Sequence Transformer Networks, an architecture designed for direct learning of invariances in time-series data.
  • Applied the Sequence Transformer Network to the task of predicting in-hospital mortality.
  • Compared performance against a baseline one-dimensional CNN model.

Main Results:

  • The Sequence Transformer Network achieved a higher Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.851 compared to the baseline CNN's AUROC of 0.838.
  • Demonstrated the model's capability to learn valuable invariances directly from clinical time-series data.
  • Indicated a potential improvement in predictive accuracy for clinical tasks.

Conclusions:

  • Sequence Transformer Networks offer a promising approach for analyzing clinical time-series data by learning complex invariances.
  • This method reduces the need for manual identification of data invariances, simplifying the preprocessing pipeline.
  • The findings suggest that end-to-end learning of invariances can enhance predictive performance in critical healthcare applications like mortality prediction.