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Association mapping in biomedical time series via statistically significant shapelet mining.

Christian Bock1,2, Thomas Gumbsch1,2, Michael Moor1,2

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This study introduces a new method to find statistically significant patterns in patient vital signs, improving early sepsis detection. The approach enhances biomarker interpretability and predictive accuracy for clinical endpoints.

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Area of Science:

  • Biomedical Informatics
  • Machine Learning
  • Time Series Analysis

Background:

  • Intensive care units collect vital signs, offering potential for patient biomarker discovery.
  • Existing biomarkers often lack predictive performance or explanatory power.
  • Time series analysis, particularly shapelet discovery, shows promise for identifying predictive subsequences in data.

Purpose of the Study:

  • To develop a novel, scalable method for discovering statistically significant shapelets in time series data.
  • To improve the interpretability and accuracy of physiological biomarkers for clinical endpoints.
  • To identify interpretable and validated physiological signatures for predicting patient phenotypes.

Main Methods:

  • A new scalable method for scanning time series to identify statistically significant discriminative patterns (shapelets).
  • Evaluation of shapelet significance using statistical tests, addressing multiple hypothesis testing with Tarone's method.
  • Pruning of untestable shapelet candidates to enhance efficiency.

Main Results:

  • The method successfully identified statistically significant patterns in heart rate, respiratory rate, and systolic blood pressure.
  • Discovered patterns serve as indicators for the severity of future sepsis events.
  • The approach combines high predictive performance with interpretable shapelet features.

Conclusions:

  • The developed method offers a statistically validated approach to discover interpretable physiological biomarkers.
  • This technique can enhance the prediction of clinical endpoints, such as sepsis severity, using routinely collected patient data.
  • The publicly available method and scripts facilitate reproducibility and further research in biomedical time series analysis.