@inproceedings{f079d5aff7154daab77c0233b712767e,
title = "Prognosticating Fetal Growth Restriction and Small for Gestational Age by Medical History",
abstract = "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.",
keywords = "deep learning, electronic health records, Fetal growth restriction, machine learning, risk prediction, small for gestational age",
author = "Herdiantri Sufriyana and Amani, {Fariska Zata} and {Al Hajiri}, {Aufar Zimamuz Zaman} and Wu, {Yu Wei} and Su, {Emily Chia Yu}",
note = "Publisher Copyright: {\textcopyright} 2024 International Medical Informatics Association (IMIA) and IOS Press.; 19th World Congress on Medical and Health Informatics, MedInfo 2023 ; Conference date: 08-07-2023 Through 12-07-2023",
year = "2024",
month = jan,
day = "25",
doi = "10.3233/SHTI231063",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "740--744",
editor = "Jen Bichel-Findlay and Paula Otero and Philip Scott and Elaine Huesing",
booktitle = "MEDINFO 2023 - The Future is Accessible",
address = "荷蘭",
}