Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History

Herdiantri Sufriyana, Fariska Zata Amani, Aufar Zimamuz Zaman Al Hajiri, Yu Wei Wu, Emily Chia Yu Su

Research output: Contribution to journalArticlepeer-review


This study aimed to develop and externally validate a prognostic prediction model for screening fetal growth restriction (FGR)/small for gestational age (SGA) using medical history. From a nationwide health insurance database (n=1,697,452), we retrospectively selected visits of 12-to-55-year-old females to healthcare providers. This study used machine learning (including deep learning) and 54 medical-history predictors. The best model was a deep-insight visible neural network (DI-VNN). It had area under the curve of receiver operating characteristics (AUROC) 0.742 (95% CI 0.734 to 0.750) and a sensitivity of 49.09% (95% CI 47.60% to 50.58% at with 95% specificity). Our model used medical history for screening FGR/SGA with moderate accuracy by DI-VNN. In future work, we will compare this model with those from systematically-reviewed, previous studies and evaluate if this model's usage impacts patient outcomes.

Original languageEnglish
Pages (from-to)740-744
Number of pages5
JournalStudies in Health Technology and Informatics
Publication statusPublished - Jan 25 2024


  • deep learning
  • electronic health records
  • Fetal growth restriction
  • machine learning
  • risk prediction
  • small for gestational age

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management


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