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相关概念视频

Classification of Illness01:17

Classification of Illness

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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...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Classification of Systems-II01:31

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Classification of Systems-I01:26

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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The connective tissues have different properties and functions in the human body. They are broadly categorized into proper, supporting, or fluid connective tissues.
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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医学专业分类基于半对抗性数据增强的基础上

Huan Zhang1,2, Dong Zhu1, Hao Tan1,2

  • 1Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, China.

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此摘要是机器生成的。

这项研究引入了一种新的数据增强技术,使用对抗性攻击来改进来自电子健康记录 (EHR) 的自动化医疗专业分类. 该方法提高了不平衡数据集的准确性和F1得分,有助于临床实践.

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科学领域:

  • 医疗信息学 医疗信息学
  • 自然语言处理自然语言处理.
  • 机器学习 机器学习

背景情况:

  • 采用电子健康记录 (EHR) 需要自动化医疗专业分类,以有效地检索数据和支持临床决策.
  • 当前的挑战包括不平衡和不足的医疗笔记数据,多类分类的复杂性,以及难以消除识别敏感信息的困难.

研究的目的:

  • 提出一种使用对抗性攻击的新型数据增强方法,以解决医学专业分类中的数据限制.
  • 设计一个包含概率学名词信息和信心再计算的分类框架,以提高准确性.

主要方法:

  • 一种基于对抗性攻击的数据增强方法,生成半对抗性示例以扩大训练数据覆盖范围.
  • 一个分类框架,将概率学名词信息与信任再计算后软max层集成在一起.
  • 在一个18类,高度不平衡的数据集上进行验证.

主要成果:

  • 与四种基准方法相比,拟议的方法显著提高了准确性和F1得分.
  • 在准确性和F1得分方面,平均有14.9%的改善.
  • 该技术有效地扩大了培训数据的决策空间覆盖范围.

结论:

  • 基于对抗性攻击的数据增强和基于名词的概率分类框架为不平衡的医学专业分类提供了强大的解决方案.
  • 这种方法提高了用于临床应用的EHR数据分析的效率和可靠性.