背景:对于妊娠早期的女性,建议羊膜穿刺术或绒毛膜绒毛取样进行筛查。随着时间的推移,机器学习显示出越来越高的准确性,并在增强决策方面找到了许多应用。病人护理,护理和助产服务的质量。本研究旨在开发一种利用机器学习技术的最佳学习模型,特别是神经网络,预测染色体异常并评估其预测效能。
■这项横断面研究将在马什哈德的助产诊所进行,2024年伊朗。数据将从高危人群中的350名孕妇中收集,这些孕妇在怀孕的前三个月(11-14周)接受了筛查测试。收集的信息包括产妇年龄,BMI,吸烟习惯,21三体和其他染色体疾病的历史,CRL和NT水平,PAPP-A和B-HCG水平,胰岛素依赖型糖尿病的存在,以及妊娠是否由IVF引起。该研究在诊所就诊期间对妇女进行了随访,并跟踪了羊膜穿刺术的结果。采样基于便利采样,并使用特征和筛查/羊膜穿刺术结果的清单收集数据。预处理后,进行特征提取以识别和预测相关特征。使用K折交叉验证对模型进行训练和评估。
结论:人们对利用人工智能方法的兴趣与日俱增,比如机器学习和深度学习,护理和助产。这强调了护士和助产士必须精通人工智能方法及其医疗保健应用。开发机器学习模型可能是有益的,特别关注神经网络,用于预测染色体异常。
■IR.MUMS.护士.REC.1402.134.
大约3%的新生儿受到先天性异常和遗传性疾病的影响,导致残疾和死亡。在活产中,根据该国的出生率,预计约有3000例唐氏综合症(21三体)。携带唐氏综合症胎儿的孕妇面临怀孕并发症的风险增加。人工智能方法,比如机器学习和深度学习,用于护理和助产,以改善决策,病人护理,和研究。护士需要积极参与基于AI的决策支持系统的开发和实施。此外,护士和助产士应在评估基于人工智能的技术在专业实践中的有效性方面发挥关键作用。
BACKGROUND: For women in the first trimester, amniocentesis or chorionic villus sampling is recommended for screening. Machine learning has shown increased accuracy over time and finds numerous applications in enhancing decision-making, patient care, and service quality in nursing and midwifery. This study aims to develop an optimal learning model utilizing machine learning techniques, particularly neural networks, to predict chromosomal abnormalities and evaluate their predictive efficacy.
UNASSIGNED: This cross-sectional study will be conducted in midwifery clinics in Mashhad, Iran in 2024. The data will be collected from 350 pregnant women in the high-risk group who underwent screening tests in the first trimester (between 11-14 weeks) of pregnancy. Information collected includes maternal age, BMI, smoking habits, history of trisomy 21 and other chromosomal disorders, CRL and NT levels, PAPP-A and B-HCG levels, presence of insulin-dependent diabetes, and whether the pregnancy resulted from IVF. The study follows up with the women during their clinic visits and tracks the results of amniocentesis. Sampling is based on Convenience Sampling, and data is gathered using a checklist of characteristics and screening/amniocentesis results. After preprocessing, feature extraction is conducted to identify and predict relevant features. The model is trained and evaluated using K-fold cross-validation.
CONCLUSIONS: There is a growing interest in utilizing artificial intelligence methods, like machine learning and deep learning, in nursing and midwifery. This underscores the critical necessity for nurses and midwives to be well-versed in artificial intelligence methods and their healthcare applications. It can be beneficial to develop a machine learning model, specifically focusing on neural networks, for predicting chromosomal abnormalities.
UNASSIGNED: IR.MUMS.NURSE.REC. 1402.134.
Approximately 3% of newborns are affected by congenital abnormalities and genetic diseases, leading to disability and death. Among live births, around 3000 cases of Down syndrome (trisomy 21) can be expected based on the country\'s birth rate. Pregnant women carrying fetuses with Down syndrome face an increased risk of pregnancy complications. Artificial intelligence methods, such as machine learning and deep learning, are being used in nursing and midwifery to improve decision-making, patient care, and research. Nurses need to actively participate in the development and implementation of AI-based decision support systems. Additionally, nurses and midwives should play a key role in evaluating the effectiveness of artificial intelligence-based technologies in professional practice.