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

Abstract

Background Preterm birth (PTB), a common pregnancy complication, is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly affects around 15 million children annually worldwide. Conventional approaches to predict PTB lack reliable predictive power, leaving >50% of cases undetected. Recently, machine learning (ML) models have shown potential as an appropriate complementary approach for PTB prediction using health records (HRs). Objective This study aimed to systematically review the literature concerned with PTB prediction using HR data and the ML approach. Methods This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. A comprehensive search was performed in 7 bibliographic databases until May 15, 2021. The quality of the studies was assessed, and descriptive information, including descriptive characteristics of the data, ML modeling processes, and model performance, was extracted and reported. Results A total of 732 papers were screened through title and abstract. Of these 732 studies, 23 (3.1%) were screened by full text, resulting in 13 (1.8%) papers that met the inclusion criteria. The sample size varied from a minimum value of 274 to a maximum of 1,400,000. The time length for which data were extracted varied from 1 to 11 years, and the oldest and newest data were related to 1988 and 2018, respectively. Population, data set, and ML models’ characteristics were assessed, and the performance of the model was often reported based on metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve. Conclusions Various ML models used for different HR data indicated potential for PTB prediction. However, evaluation metrics, software and package used, data size and type, selected features, and importantly data management method often remain unjustified, threatening the reliability, performance, and internal or external validity of the model. To understand the usefulness of ML in covering the existing gap, future studies are also suggested to compare it with a conventional method on the same data set.

Chronic glomerulonephritis and pregnancy: predictors of preterm birth

“…[10][11][12]. Неоднократно создавались прогностические модели ПР (чаще всего для общей популяции беременных, реже -для женщин с прегестационным сахарным диабетом), в том числе с применением искусственного интеллекта [13][14][15][16]. Однако мы не нашли в литературе моделей прогнозирования ПР у пациенток с ХГН.…”

Gestational Diabetes Mellitus And Autonomic Dysfunction: Impact On Preeclampsia Risk

“…In comparison to the normotensive pregnant controls, the hypertensive group had higher LF/HF and decreased HF and RMSSD. 11 Preeclampsia has also been associated with autonomic dysfunction, which is characterised by an imbalance in the activity of the sympathetic and parasympathetic nerve systems. Our comprehensive review's key result is that autonomic dysfunction, which shows up as higher sympathetic tone, decreased parasympathetic tone, and decreased baroreflex gain, appears to be a common indicator in pregnant women who may be preeclamptic.…”

Implemented machine learning tools to inform decision-making for patient care in hospital settings: a scoping review

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“…Artificial intelligence (AI) techniques have gained popularity within healthcare in recent years. [1][2][3][4] AI techniques consist of automated systems requiring 'intelligence' to perform tasks. Machine learning is an AI method that 'refers to the process of developing systems with the ability to learn from and make predictions using data'.…”