College of Clinical Medicine, Tianjin Medical University, Tianjin 300070, China.
Pediatric Department, Affiliated Hospital of Weifang Medical University, Weifang 261031, Shandong, China.
J Healthc Eng. 2021 Oct 16;2021:7227928. doi: 10.1155/2021/7227928. eCollection 2021.
This article proposes that machine learning can break through the technical limitations of the linear growth test for the early physique of infants smaller than gestational age and can accurately calculate and predict the consequences of the disease. For testing the linear growth of the early physique of infants smaller than gestational age, the data collection and judgment are carried out according to the computer analysis method. Experimental results show that 47.3% of infants younger than gestational age may have suffocation. The experimental subjects designed in this study are small-for-gestational-age infants who were hospitalized in the neonatal intensive care unit from January 2020 to January 2021. According to the relationship between gestational age and birth weight, the survey subjects were divided into two groups: early group and late group. Male and female small-for-gestational-age infants accounted for 68% and 32%, respectively. Among them, the proportion of early gestational age was the most, with more boys than girls, and sick singleton was more than twins. In the early group, the incidence was 52.1% for neonatal asphyxia, 22.5% for feeding intolerance, 14.8% for intracranial hemorrhage, 6.3% for scleredema, 24.7% for neonatal hyperbilirubinemia, 24.6% for hypoglycemia, 1.1% for apnea, and 3.2% for respiratory distress syndrome. Infants develop differently at different stages of corrected gestational age. The incidence of low body weight (6%) after correction for 3 months was significantly reduced compared with correction for gestational age, and the difference was statistically significant ( < 0.05). The nutrient absorption of infants younger than gestational age can promote physical catch-up growth, physical development, and neurodevelopment. Therefore, the physical growth of infants younger than gestational age requires supplementation that focuses on nutrition.
这篇文章提出，机器学习可以突破针对小于胎龄儿早期体格的线性生长测试的技术限制，并能够准确计算和预测疾病的后果。针对小于胎龄儿早期体格的线性生长测试，采用计算机分析方法进行数据收集和判断。实验结果显示，47.3%的小于胎龄儿可能存在窒息的情况。本研究设计的实验对象为 2020 年 1 月至 2021 年 1 月在新生儿重症监护病房住院的小于胎龄儿。根据胎龄与出生体重的关系，将调查对象分为两组：早期组和晚期组。小于胎龄儿中男婴占 68%，女婴占 32%。其中，早期胎龄比例最高，男婴多于女婴，且病儿多为单胎，双胞胎较少。早期组新生儿窒息发生率为 52.1%，喂养不耐受发生率为 22.5%，颅内出血发生率为 14.8%，硬肿发生率为 6.3%，新生儿高胆红素血症发生率为 24.7%，低血糖发生率为 24.6%，呼吸暂停发生率为 1.1%，呼吸窘迫综合征发生率为 3.2%。小于胎龄儿在不同矫正胎龄阶段发育情况不同。矫正 3 个月后低体重发生率（6%）较矫正胎龄时显著降低，差异有统计学意义（ < 0.05）。小于胎龄儿的营养吸收能促进追赶性生长、体格发育和神经发育。因此，小于胎龄儿的体格生长需要注重营养补充。
