Abstract
Prevention of fetal growth restriction/small for gestational age (FGR/SGA) is adequate if screening is accurate. Ultrasound and biomarkers can achieve this goal; however, both are often inaccessible. This study aimed to develop, validate, and deploy a prognostic prediction model for screening FGR/SGA using only medical history. From a nationwide health insurance database (n = 1,697,452), we retrospectively selected visits to 22,024 healthcare providers of primary, secondary, and tertiary care. This study used machine learning (including deep learning) to develop prediction models using 54 medical-history predictors. After evaluating model calibration, clinical utility, and explainability, we selected the best by discrimination ability. We also externally validated the models using geographical and temporal splits of ~ 20% of the selected visits. The models were also compared with those from previous studies, which were rigorously selected by a systematic review of Pubmed, Scopus, and Web of Science. We selected 169,746 subjects with 507,319 visits for predictive modeling from the database, which were 12-to-55-year-old female insurance holders who used the healthcare services. The best prediction model was a deep-insight visible neural network. It had an area under the receiver operating characteristics curve of 0.742 (95% confidence interval 0.734 to 0.750) and a sensitivity of 49.09% (95% confidence interval 47.60-50.58% using a threshold with 95% specificity). The model was competitive against the previous models of 30 eligible studies of 381 records, including those using either ultrasound or biomarker measurements. We deployed a web application to apply the model. Our model used only medical history to improve accessibility for FGR/SGA screening. However, future studies are warranted to evaluate if this model's usage impacts patient outcomes.