Tran DC, Dang AL, Hoang TNL, Nguyen CT, Le TMP, Dinh TNM, Tran VA, Doan TKP, Nguyen TT. Prevalence of Thalassemia in the Vietnamese Population and Building a Clinical Decision Support System for Prenatal Screening for Thalassemia.
Mediterr J Hematol Infect Dis 2023;
15:e2023026. [PMID:
37180206 PMCID:
PMC10171208 DOI:
10.4084/mjhid.2023.026]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 04/16/2023] [Indexed: 05/16/2023] Open
Abstract
Introduction
The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems for prenatal screening of thalassemia were created. The aim of this report was to investigate the prevalence of thalassemia in the Vietnamese population, building a clinical decision support system for prenatal screening for thalassemia.
Methods
A cross-sectional study was conducted on pregnant women and their husbands visiting the Vietnam National Hospital of Obstetrics and Gynecology from October 2020 to December 2021. A total of 10112 medical records of first-time pregnant women and their husbands were collected.
Results
A clinical decision support system was built, including 2 different types of systems for prenatal screening for thalassemia (an expert system and 4 AI-based CDSS). One thousand nine hundred ninety-two cases were used to train and test machine learning models, while 1555 cases were used for specialized expert system evaluation. There were ten key variables for AI-based CDSS for machine learning. The four most important features in thalassemia screening were identified. The accuracy of the expert system and AI-based CDSS was compared. The rate of patients with Alpha thalassemia is 10.73% (1085 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. The expert system showed an accuracy of 98.45%. Among the AI-based CDSS developed, the multilayer perceptron (MLP) model was the most stable regardless of the training database (accuracy of 98,5% using all features and 97% using only the four most important features).
Conclusions
When comparing the expert system with the AI-based CDSS, the accuracy of the expert system and AI-based models was comparable. The developed expert system for prenatal thalassemia screening showed high accuracy. 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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