Prevalence of thalassemia in the Vietnamese population and building a clinical decision support system for prenatal screening for thalassemia
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Center for Prenatal Diagnosis, National Hospital of Obstetrics and Gynecology, Hanoi, VIETNAM
Department of Obstetrics and Gynecology, Hanoi Medical University, Hanoi, VIETNAM
Department of Biomedical and genetics, Hanoi Medical University, Hanoi, VIETNAM
Department of Specialized Software, Academy of Military Science and Technology, Hanoi, VIETNAM
Department of Basic Sciences in Medicine and Pharmacy, University of Medicine and Pharmacy-Vietnam National University, Hanoi, VIETNAM
Department of Pediatrics, Hanoi Medical University, Hanoi, VIETNAM
Department of Pediatrics, Hanoi Medical University Hospital, Hanoi, VIETNAM
Clinical Genetics Center, Hanoi Medical University Hospital, Hanoi, VIETNAM
Online publication date: 2023-04-14
Publication date: 2023-07-01
Electron J Gen Med 2023;20(4):em501
The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems (CDSSs) for prenatal screening of thalassemia were created. A cross-sectional study was conducted on pregnant women and their husbands visiting from October 2020 to December 2021. A total of 10,112 medical records of first-time pregnant women and their husbands were collected. CDSS including two different types of systems for prenatal screening for thalassemia (expert system [ES] and four artificial intelligence [AI]-based CDSS) was built. 1,992 cases were used to train and test machine learning (ML) models while 1,555 cases were used for specialized ES evaluation. There were 10 key variables for AI-based CDSS for ML. The four most important features in thalassemia screening were identified. Accuracy of ES and AI-based CDSS was compared. The rate of patients with alpha thalassemia is 10.73% (1,085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. ES showed an accuracy of 98.45%. Among AI-based CDSS developed, multilayer perceptron model was the most stable regardless of the training database (accuracy of 98.50% using all features and 97.00% using only the four most important features). AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice.
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