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Associative Learning01:27

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
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Active transport is a critical biological process that allows cells to move solutes against an electrochemical gradient. This process requires direct energy input and is characterized by its selectivity, saturability, and susceptibility to competitive inhibition.
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Immunity, along with the ability to limit pathogen growth to prevent significant body tissue damage, can be gained either by (1) actively developing an immune response within the individual after exposure to a pathogen or after getting vaccinated or (2) passively transferring immune components from an immune individual to one who is nonimmune. Both these forms of immunity can be found naturally and in medical practices.
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相关实验视频

Updated: Jun 4, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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临床概念注释与上下文词语嵌入在积极的转移学习环境中的注释.

Asim Abbas1, Mark Lee1, Niloofer Shanavas2

  • 1School of Computer Science, University of Birmingham, Birmingham, UK.

Digital health
|December 23, 2024
PubMed
概括

这项研究引入了用于临床概念提取的积极学习方法,提高了使用SCIBERT和CNNs等模型对医疗数据进行分类的效率和准确性.

关键词:
临床概念提取 临床概念提取积极的学习转移学习.临床概念注释临床概念注释语境词语嵌入 语境词语嵌入提取信息 提取信息大型语言模型.

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科学领域:

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

背景情况:

  • 非结构化的临床数据对自动信息提取提出了挑战.
  • 准确识别临床概念 (问题,治疗,测试) 对于医疗保健应用至关重要.

研究的目的:

  • 开发和评估用于自动化临床概念提取的积极学习方法.
  • 以高精度对临床概念进行分类,并从非结构化的电子健康记录中召回.

主要方法:

  • 利用基于词汇的方法进行初始数据标签,以支持主动学习.
  • 在概念分类中使用与BERT变体 (ClinicalBERT,DistilBERT,SCIBERT) 嵌入相似性的上下文词.
  • 训练有素的深度学习模型,包括使用主动学习的卷积神经网络 (CNN) 和大型语言模型 (LLM).

主要成果:

  • 斯基伯特在主动转移学习方面表现强,F1得分达到73.97%.
  • 用各种嵌入式 (BERTBase,DistilBERT,SCIBERT,ClinicalBERT) 进行训练的CNN达到89-93%的测试准确度.
  • 临床BERT作为一个LLM实现了最高的性能96%的测试准确度.

结论:

  • 与SCIBERT和CNNs集成的积极学习方法改善了临床概念提取.
  • 该方法提高了注释效率,并保持了高准确度,显示了临床应用的巨大潜力.