JMIR Publications

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. Clinical Trial: n.a.