Machine learning-based clinical decision support systems for pregnancy care: A systematic review

• Published in: International journal of medical informatics (Shannon, Ireland) Ireland), 2023-05, Vol.173, p.105040-105040, Article 105040
• Main Authors: Du, Yuhan, McNestry, Catherine, Wei, Lan, Antoniadi, Anna Markella, McAuliffe, Fionnuala M., Mooney, Catherine
• Format: Article
• Language: English
• Subjects: Algorithms; Clinical decision support system; Decision Support Systems, Clinical; Delivery of Health Care; Empirical Research; Explainable artificial intelligence; Female; Humans; Machine Learning; Pregnancy; Pregnancy care
• ISSN: 1386-5056; 1872-8243
• DOI: 10.1016/j.ijmedinf.2023.105040

Clinical decision support systems (CDSSs) can provide various functions and advantages to healthcare delivery. Quality healthcare during pregnancy and childbirth is of vital importance, and machine learning-based CDSSs have shown positive impact on pregnancy care. This paper aims to investigate what has been done in CDSSs in the context of pregnancy care using machine learning, and what aspects require attention from future researchers. We conducted a systematic review of existing literature following a structured process of literature search, paper selection and filtering, and data extraction and synthesis. 17 research papers were identified on the topic of CDSS development for different aspects of pregnancy care using various machine learning algorithms. We discovered an overall lack of explainability in the proposed models. We also observed a lack of experimentation, external validation and discussion around culture, ethnicity and race from the source data, with most studies using data from a single centre or country, and an overall lack of awareness of applicability and generalisability of the CDSSs regarding different populations. Finally, we found a gap between machine learning practices and CDSS implementation, and an overall lack of user testing. Machine learning-based CDSSs are still under-explored in the context of pregnancy care. Despite the open problems that remain, the few studies that tested a CDSS for pregnancy care reported positive effects, reinforcing the potential of such systems to improve clinical practice. We encourage future researchers to take into consideration the aspects we identified in order for their work to translate into clinical use. •We identified 17 papers on machine learning-based CDSSs for pregnancy care.•Increasing trend of using black-box methods; the need for explainability identified.•Overall lack of experimentation and discussion around culture/ethnicity/race.•Limited implementation and user testing.•Positive feedback from clinicians on the CDSSs that were tested.