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

Yichao Zhang; Sha Lu; Yina Wu; Wensheng Hu; Zhenming Yuan

doi:10.2196/33835

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

JMIR Med Inform. 2022 Jun 13;10(6):e33835. doi: 10.2196/33835.

Authors

Yichao Zhang^#¹, Sha Lu^#^{2

3}, Yina Wu¹, Wensheng Hu^{2

3}, Zhenming Yuan¹

Affiliations

¹ Hangzhou Normal University, Hangzhou, China.
² Department of Obstetrics and Gynecology, Hangzhou Women's Hospital, Hangzhou, China.
³ Department of Obstetrics and Gynecology, The Affiliated Hangzhou Women's Hospital of Hangzhou Normal University, Hangzhou, China.

^# Contributed equally.

PMID: 35700004
PMCID: PMC9237764
DOI: 10.2196/33835

Abstract

Background: Globally, the preterm birth rate has tended to increase over time. Ultrasonography cervical-length assessment is considered to be the most effective screening method for preterm birth, but routine, universal cervical-length screening remains controversial because of its cost.

Objective: We used obstetric data to analyze and assess the risk of preterm birth. A machine learning model based on time-series technology was used to analyze regular, repeated obstetric examination records during pregnancy to improve the performance of the preterm birth screening model.

Methods: This study attempts to use continuous electronic medical record (EMR) data from pregnant women to construct a preterm birth prediction classifier based on long short-term memory (LSTM) networks. Clinical data were collected from 5187 pregnant Chinese women who gave birth with natural vaginal delivery. The data included more than 25,000 obstetric EMRs from the early trimester to 28 weeks of gestation. The area under the curve (AUC), accuracy, sensitivity, and specificity were used to assess the performance of the prediction model.

Results: Compared with a traditional cross-sectional study, the LSTM model in this time-series study had better overall prediction ability and a lower misdiagnosis rate at the same detection rate. Accuracy was 0.739, sensitivity was 0.407, specificity was 0.982, and the AUC was 0.651. Important-feature identification indicated that blood pressure, blood glucose, lipids, uric acid, and other metabolic factors were important factors related to preterm birth.

Conclusions: The results of this study will be helpful to the formulation of guidelines for the prevention and treatment of preterm birth, and will help clinicians make correct decisions during obstetric examinations. The time-series model has advantages for preterm birth prediction.

Keywords: electronic medical records; pregnant healthcare; preterm birth prediction; temporal data mining.

©Yichao Zhang, Sha Lu, Yina Wu, Wensheng Hu, Zhenming Yuan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 13.06.2022.