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

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.

Unlocking Maternal Outcome Prediction Potential: A Comprehensive Analysis of the ConvXGB Model Integrating XGBoost and Deep Learning” at Evolving Systems.

“…Review of Related Works The computational learning approach has been proven to be a reliable tool in predicting various maternal outcomes, such as full-term delivery (Predicting induced labour outcomes for full-term pregnancies using an Intuitionistic Fuzzy Approach for maternal outcome prediction [12], [13]), miscarriage (proposed early prediction for both miscarriage and threatened miscarriage [3], [14], [15]), mortality (implemented machine learning techniques to forecast in-hospital mortality [16]- [18]),placenta previa, preterm delivery (utilizing machine learning for the early prediction of spontaneous preterm birth [19]- [22]), stillbirth (Data-Driven Stillbirth Prediction in Pregnancy [23]), and Urinary Tract Infection (UTI) exploring machine learning algorithms for predicting UTI [24]- [27]. It is encouraging to see the advancements in this field, which have the potential to improve the health and safety of expectant mothers.…”

Preterm Birth Risk Stratification through Longitudinal Heart Rate and HRV Monitoring in Daily Life

“…This complexity explains why the existing methodologies for estimating and predicting PTB mostly rely on conventional risk factors derived from electronic health records (EHR) [14][15][16][17] . However, leveraging patient data from EHRs faces challenges, including incomplete records for pregnant women due to socio-economic barriers 18 .…”