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相关概念视频

Changes in Skin Color: Clinical Perspectives01:14

Changes in Skin Color: Clinical Perspectives

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The first thing a clinician sees is the skin, so the examination of the skin should be part of any thorough physical examination. Most skin disorders are relatively benign, but a few, including melanomas, can be fatal if untreated. A couple of the more noticeable disorders, albinism and vitiligo, affect the appearance of the skin and its accessory organs.
Albinism
Albinism is a genetic disorder that affects (completely or partially) the coloring of skin, hair, and eyes. The defect is primarily...
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相关实验视频

Updated: Sep 10, 2025

How to Administer Near-Infrared Spectroscopy in Critically ill Neonates, Infants, and Children
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新生儿黄需要光疗的危险因素:机器学习方法

Yunjin Choi1, Sunyoung Park1, Hyungbok Lee1

  • 1Nursing Department, Seoul National University Hospital, Seoul 03038, Republic of Korea.

Children (Basel, Switzerland)
|August 28, 2025
PubMed
概括

机器学习有效地识别出需要照明治疗黄的新生儿. 关键预测因素包括分娩方式,养模式和母体因素,有助于早期干预以获得更好的婴儿结果.

科学领域:

  • 新生儿医学
  • 数据科学
  • 预测分析

背景情况:

  • 新生儿黄是一种常见的疾病,如果不及时治疗,可能导致严重的高胆红素血症.
  • 尽管目前有指导方针,但对高风险的新生儿的早期识别仍然是一个临床挑战.
  • 现有的诊断方法可能无法捕捉重症新生儿黄的全部风险因素.

研究的目的:

  • 确定与需要光疗的新生儿黄相关的关键孕产妇和新生儿风险因素.
  • 开发和验证用于预测新生儿光疗需求的机器学习模型.
  • 增强早期临床决策,以管理新生儿高 bilirubinemia.

主要方法:

  • 2017-2022年8242名新生儿电子病历的回顾性分析.
  • 应用机器学习算法,包括XGBoost,以预测光疗需求.
  • 使用SHAP值来解释预测模型并确定关键风险因素.

主要成果:

  • 分娩方式,新生儿养指标 (配方剂摄入量,母乳养频率),母体BMI和母体白细胞计数被确定为重要的预测指标.
  • 剖腹产和较低的出生体重与需要光疗的可能性增加.
  • XGBoost模型以0.911的AUROC实现了高预测性能.
关键词:
电子医疗记录血清过高 血清过高机器学习新生儿黄光疗法

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结论:

  • 经过围产期数据训练的机器学习模型可以准确预测新生儿黄需要光疗的风险.
  • 这些预测模型为早期临床干预提供了有价值的工具,
  • 将机器学习纳入临床实践可以支持关于新生儿黄光疗的及时决策.