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The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study

Yichao Zhang1, Sha Lu2,3, Yina Wu1

  • 1Hangzhou Normal University, Hangzhou, China.

JMIR Medical Informatics
|June 14, 2022
PubMed

Insights

A new time-series machine learning model using electronic medical records (EMR) shows improved preterm birth prediction. This approach identifies metabolic factors as key indicators, aiding clinical decisions for preventing premature birth.

Area of Science:

  • Obstetrics and Gynecology
  • Medical Informatics
  • Machine Learning in Healthcare

Background:

  • Global preterm birth rates are rising, necessitating effective screening.
  • Cervical-length ultrasonography is effective but costly for universal screening.

Purpose of the Study:

  • To develop an improved preterm birth prediction model using time-series analysis of obstetric data.
  • To leverage continuous electronic medical record (EMR) data for enhanced screening.

Main Methods:

  • Utilized long short-term memory (LSTM) networks on EMR data from 5187 pregnant women.
  • Analyzed over 25,000 obstetric records from early pregnancy to 28 weeks.
  • Assessed model performance using Area Under the Curve (AUC), accuracy, sensitivity, and specificity.

Main Results:

  • The time-series LSTM model outperformed traditional cross-sectional methods in prediction.
  • Achieved an accuracy of 0.739, sensitivity of 0.407, specificity of 0.982, and AUC of 0.651.
  • Identified blood pressure, glucose, lipids, and uric acid as significant predictors of preterm birth.

Conclusions:

  • Time-series modeling offers advantages for preterm birth prediction.
  • Findings can inform guidelines for preterm birth prevention and treatment.
  • The model aids clinicians in making informed decisions during obstetric care.
Abstract

Keywords:
electronic medical recordspregnant healthcarepreterm birth predictiontemporal data mining

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