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相关概念视频

Language Development01:22

Language Development

359
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
359
Classification of Illness01:17

Classification of Illness

7.5K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.5K
The Nativist Approach01:21

The Nativist Approach

48
The nativist approach to infant cognitive development proposes that infants are born with inherent knowledge structures that allow them to interpret the world almost immediately. This perspective contrasts with earlier developmental theories, such as those proposed by Jean Piaget, which emphasized a more gradual acquisition of cognitive abilities through interaction with the environment. One key concept in this approach is object permanence — the understanding that objects continue to...
48

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相关实验视频

Updated: Jun 29, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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从临床笔记中对婴儿早期养状况进行分类,使用自然语言处理和机器学习.

Dominick J Lemas1,2, Xinsong Du3,4, Masoud Rouhizadeh5,6

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida College of Medicine, 2004 Mowry Road, Clinical and Translational Research Building, Gainesville, FL, 32610, USA. djlemas@ufl.edu.

Scientific reports
|April 3, 2024
PubMed
概括
此摘要是机器生成的。

这项研究开发了自然语言处理 (NLP) 模型,从电子健康记录中准确预测婴儿养状态. XGBoost模型的准确率达到90.1%,使得公共卫生干预措施的早期识别成为可能.

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相关实验视频

Last Updated: Jun 29, 2025

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科学领域:

  • 计算语言学计算语言学
  • 医疗信息学 医疗信息学
  • 机器学习是机器学习.

背景情况:

  • 准确的婴儿养状态对于公共健康至关重要.
  • 电子健康记录 (EHR) 包含有价值的非结构化的临床笔记.
  • 从电子健康记录中预测养状况可以为目标干预提供信息.

研究的目的:

  • 开发和评估自然语言处理 (NLP) 和机器学习 (ML) 模型.
  • 用Epic EHR系统的临床笔记来预测婴儿养状态.
  • 使用医疗主体标题 (MeSH) 术语对养状态进行分类.

主要方法:

  • 在999个手动审查的临床笔记上训练了6个ML模型 (逻辑回归,随机森林,XGBoost,k-NN,SVC).
  • 使用TeamTat进行注释和MeSH术语进行分类.
  • 基于准确性,精度,回忆和F1分数的评估模型.

主要成果:

  • XGBoost模型以90.1%的准确性展示了卓越的性能.
  • 实现了宏观平均精度,回忆和F1分数分别为90.3%,90.1%和90.1%.
  • 成功地将婴儿养状态分为母乳,配方奶粉/奶瓶和缺少的类别.

结论:

  • 根据非结构化EHR数据,NLP有效地对婴儿养状况进行分类.
  • 通过NLP早期识别可以支持精确的公共卫生倡议.
  • 这些发现可以帮助改善产后患者的哺乳支持.