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首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Risk groups in children under six months of age using self-organizing maps
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Risk groups in children under six months of age using self-organizing maps

机译：使用自组织图绘制六个月以下儿童的危险人群

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Fetal and infant growth tends to follow irregular patterns and, particularly in developing countries, these patterns are greatly influenced by unfavorable living conditions and interactions with complications during pregnancy. The aim of this study was to identify groups of children with different risk profiles for growth development. The study sample comprised 496 girls and 508 boys under six months of age from 27 pediatric primary health care units in the city of Rio de Janeiro, Brazil. Data were obtained through interviews with the mothers and by reviewing each child's health card. An unsupervised learning, know as a self-organizing map (SOM) and a K-means algorithm were used for cluster analysis to identify groups of children. Four groups of infants were identified. The first (139) consisted of infants born exclusively by cesarean delivery, and their mothers were exclusively multiparous; the highest prevalences of prematurity and low birthweight, a high prevalence of exclusive breastfeeding and a low proportion of hospitalization were observed for this group. The second (247 infants) and the third (298 infants) groups had the best and worst perinatal and infant health indicators, respectively. The infants of the fourth group (318) were born heavier, had a low prevalence of exclusive breastfeeding, and had a higher rate of hospitalization. Using a SOM, it was possible to identify children with common features, although no differences between groups were found with respect to the adequacy of postnatal weight. Pregnant women and children with characteristics similar to those of group 3 require early intervention and more attention in public policy.
机译：胎儿和婴儿的生长趋于遵循不规则的模式，尤其是在发展中国家，这些模式受到不利的生活条件以及怀孕期间并发症的相互作用的极大影响。这项研究的目的是确定成长发育风险不同的儿童群体。该研究样本包括来自巴西里约热内卢市的27个儿科初级卫生保健部门的496个女孩和508个男孩，年龄在6个月以下。数据是通过与母亲的访谈以及查看每个孩子的健康卡获得的。使用无监督学习（称为自组织图（SOM））和K-means算法进行聚类分析，以识别儿童组。确定了四组婴儿。第一个（139）包括仅通过剖腹产出生的婴儿，其母亲完全是多胎的。在该组中，早产和低出生体重的最高发生率，纯母乳喂养的高发生率和住院率较低。第二组（247例婴儿）和第三组（298例婴儿）的围产期和婴儿健康指标分别为最佳和最差。第四组婴儿（318）出生较重，纯母乳喂养的患病率较低，住院率更高。使用SOM可以识别具有共同特征的儿童，尽管两组之间在出生后体重的适当性方面没有发现差异。具有与第3组相似特征的孕妇和儿童需要早期干预并在公共政策中得到更多关注。

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