2021
DOI: 10.32539/bsm.v5i4.376
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Congenital Heart Diseases in Pregnancy

Abstract: This research aims to shed light into congenital heart diseases, the pathophysiology, and the ultrasonographic findings of congenital heart diseases. Congenital heart diseases are a major health concern, affecting 1.35 million children born every year. Ventricular septal defect, atrial septal defect, and atrioventricular septal defect are found in 57.9% cases of congenital heart diseases. The risk factors include consanguineous marriage, family history of congenital heart diseases, old maternal and paternal ag… Show more

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“…Therefore, the development of a prognostic model for heart failure patients based on echocardiographic metrics is of great value to assist clinicians in the treatment of hospitalized patients and the daily management of patients after discharge. Comparative models were developed by applying machine learning algorithms with traditional statistical methods, that is, logistic linear regression, respectively, and, by comparing the values of the two models developed for the assessment of mortality and the risk of cardiovascular events in patients, it was concluded that comparative risk models with good comparative power and risk identification could be obtained by machine learning algorithms [ 7 ]. The purpose of this paper is to establish a comparative model based on the echocardiographic findings of the patients, by applying the BP neural network learning algorithm jointly by applying the 7 indicators reflecting the cardiac function of the patients obtained by echocardiography, and to conduct a comparative study on the comparative results of patients with reduced ejection fraction, that is, 1-year readmission and 3-year mortality, which has high clinical research and practical application significance.…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, the development of a prognostic model for heart failure patients based on echocardiographic metrics is of great value to assist clinicians in the treatment of hospitalized patients and the daily management of patients after discharge. Comparative models were developed by applying machine learning algorithms with traditional statistical methods, that is, logistic linear regression, respectively, and, by comparing the values of the two models developed for the assessment of mortality and the risk of cardiovascular events in patients, it was concluded that comparative risk models with good comparative power and risk identification could be obtained by machine learning algorithms [ 7 ]. The purpose of this paper is to establish a comparative model based on the echocardiographic findings of the patients, by applying the BP neural network learning algorithm jointly by applying the 7 indicators reflecting the cardiac function of the patients obtained by echocardiography, and to conduct a comparative study on the comparative results of patients with reduced ejection fraction, that is, 1-year readmission and 3-year mortality, which has high clinical research and practical application significance.…”
Section: Introductionmentioning
confidence: 99%