| Autor: |
Sufriyana H; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan.; Department of Medical Physiology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Indonesia., Amani FZ; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Indonesia., Al Hajiri AZZ; Faculty of Medicine, Universitas Nahdlatul Ulama Surabaya, Indonesia., Wu YW; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan.; Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan., Su EC; Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taiwan.; Clinical Big Data Research Center, Taipei Medical University Hospital, Taiwan.; Research Center for Artificial Intelligence in Medicine, Taipei Medical University, Taiwan. |
| Abstrakt: |
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. |
