Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review (Preprint)

Abstract

BACKGROUND Preterm birth (PTB) as a common pregnancy complication is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly impacts around 15 million children annually across the world. Conventional approaches to predict PTB may neither be applicable for first-time mothers nor possess reliable predictive power. Recently, machine learning (ML) models have shown the potential as an appropriate complementary approach for PTB prediction. OBJECTIVE In this article we systematically reviewed the literature concerned with PTB prediction using ML modeling. METHODS This systematic review was conducted in accordance with the PRISMA statement. A comprehensive search was performed in seven bibliographic databases up until 15 May 2021. The quality of studies was assessed, and the descriptive information including socio-demographic characteristics, ML modeling processes, and model performance were extracted and reported. RESULTS A total of 732 papers were screened through title and abstract. Of these, 23 studies were screened by full text resulting in 13 papers that met the inclusion criteria. CONCLUSIONS We identified various ML models used for different EHR data resulting in a desirable performance for PTB prediction. However, evaluation metrics, software/package used, data size and type, and selected features, and importantly data management method often varied from study to study threatening the reliability and generalizability of the model. CLINICALTRIAL n.a.

Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms

“…Imaging tests or invasive screening have potential as effective screening methods, but remain experimental because of high cost, possible harm, and low accessibility (Bahado-Singh et al, 2019;Considine et al, 2019;Wang et al, 2019). Non-invasive screening measures using machine learning (ML) algorithms based on large-scale pregnancy surveillance data with multilevel information linkage to delivery records promises to be beneficial to support clinical decision making to predict adverse pregnancy outcomes and guide pregnancy management without any extra physiological or imaging tests (Gao et al, 2019;Sharifi-Heris et al, 2022).…”

“…To achieve an efficient prediction model, feature selection is an important process to reduce dimensionality and computing complexity, and facilitate clinical practice. There are two conventional ways to conduct feature selection: one is applying univariate analysis to select features which are highly associated with the outcome (Park et al, 2022;Nsugbe et al, 2023), another is relying on feature importance derived from ML algorithms (Sharifi-Heris et al, 2022;Espinosa et al, 2023). However, some known important features might be ignored when only relying on ML-based feature importance lists (Bose et al, 2019;Liverani et al, 2023).…”