• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biomedical Engineering: Applications, Basis and Communications >ASSESSMENT OF WEIGHT CHANGES AND SUGGESTION OF NUTRITIONAL FORMULAS FOR PREMATURELY BORN BABIES WITH CLOSEST REASONABLE CENTROIDS
【24h】

ASSESSMENT OF WEIGHT CHANGES AND SUGGESTION OF NUTRITIONAL FORMULAS FOR PREMATURELY BORN BABIES WITH CLOSEST REASONABLE CENTROIDS

机译：亲缘关系最近的早产婴儿的体重变化评估和营养配方建议

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recently, many models of applying artificial intelligence (AI) techniques into the analysis of clinical data have been proposed. Unfortunately, most models provide little help when specific "cause-effect" relation of data is not available, or even known. In this paper, an innovative method, called closest reasonable centroids (CRC), is directed to address this issue. Our present application domain was a clinical data set of the weight changes of 274 prematurely born babies who had nutritional deficiency problem and were given TPN treatments to improve their nutritional needs. Experimental result shows that the CRC's differentiability is comparable to that of the back-propagation neural networks (BPN) and better than that of statistical method. Also, from the health conditions of babies and their nutritional treatments, the proposed method can roughly predict their weight changes and provide some suggested feasible formula. All of the above results have been double confirmed by the clinicians, implicating that CRC could be used as assistant tool.
机译：最近，已经提出了许多将人工智能（AI）技术应用于临床数据分析的模型。不幸的是，当数据的特定“因果”关系不可用甚至未知时，大多数模型几乎没有帮助。在本文中，一种创新的方法称为最接近的合理质心（CRC），旨在解决这个问题。我们目前的应用领域是274名营养不足问题并接受TPN治疗以改善其营养需求的早产婴儿体重变化的临床数据集。实验结果表明，CRC的可区分性与反向传播神经网络（BPN）相当，并且优于统计方法。此外，从婴儿的健康状况及其营养治疗方法来看，该方法可以粗略地预测婴儿的体重变化，并提供一些可行的公式。以上所有结果均已得到临床医生的双重确认，暗示CRC可以用作辅助工具。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号