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End-to-end learning with interpretation on electrohysterography data to predict preterm birth.

A M Fischer1, A L Rietveld2, P W Teunissen3

  • 1Department of Computer Science, Vrije Universiteit, De Boelelaan 1105, Amsterdam, 1081 HV, The Netherlands; Department of Obstetrics and Gynecology, Amsterdam UMC Location AMC, Meibergdreef 9, Amsterdam, 1105 AZ, The Netherlands.

Computers in Biology and Medicine
|April 5, 2023
PubMed
Summary

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Deep learning models for predicting preterm birth using electrohysterography (EHG) show promise. Adding clinical data did not improve EHG model performance, but an interpretability framework was developed.

Area of Science:

  • Biomedical Engineering
  • Machine Learning in Healthcare
  • Obstetrics and Gynecology

Background:

  • Predicting preterm birth remains a clinical challenge.
  • Electrohysterography (EHG) signals uterine electrical activity, offering potential for preterm birth prediction.
  • Interpreting EHG signals requires specialized signal processing knowledge, limiting clinical utility.

Purpose of the Study:

  • To apply Deep Learning models, specifically Long Short-Term Memory (LSTM) and Temporal Convolutional Networks (TCN), to electrohysterography (EHG) data for preterm birth prediction.
  • To evaluate the impact of incorporating clinical data alongside EHG data on model performance.
  • To develop and validate an interpretability framework for time series classification models in low-data scenarios.

Main Methods:

Keywords:
Deep learningElectrohysterographyExplainable AIInterpretability frameworkMachine learningPreterm birth prediction

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  • Utilized the Term-Preterm Electrohysterogram database for model training and evaluation.
  • Implemented end-to-end Deep Learning models (LSTM and TCN) for EHG signal analysis.
  • Assessed model performance with and without the inclusion of available clinical data.
  • Developed a novel interpretability framework for time series classification.

Main Results:

  • End-to-end Deep Learning models achieved an Area Under the Curve (AUC) of 0.58, comparable to traditional machine learning models using handcrafted features.
  • The addition of clinical data to EHG data did not lead to a significant performance improvement.
  • The proposed interpretability framework demonstrated suitability for limited datasets.

Conclusions:

  • Deep Learning models offer a viable approach for analyzing EHG data for preterm birth prediction, achieving performance comparable to existing methods.
  • Clinical data, in its current form, does not enhance the predictive power of EHG-based models.
  • The developed interpretability framework provides clinicians with insights into model predictions and highlights the need for high-risk patient datasets to reduce false positives.