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Improving Translational Accuracy

Improving Translational Accuracy

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...

Classification of Illness

Classification of Illness

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...

Classification of Systems-II

Classification of Systems-II

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,

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Classification of Neurotransmitters

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...

Generalization, Discrimination, and Extinction

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...

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相关实验视频

Updated: Jul 16, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

通过使用持续预训练,提高临床注释部分分类模型的模型可转移性.

Weipeng Zhou¹, Meliha Yetisgen¹, Majid Afshar²

¹Department of Biomedical Informatics and Medical Education, School of Medicine, University of Washington-Seattle, Seattle, WA, United States.

Journal of the American Medical Informatics Association : JAMIA

|September 19, 2023

概括

此摘要是机器生成的。

持续的预训练提高了临床注释部分分类的可转移性. 这种方法与少量域内数据相结合,可显著提高不同机构的模型性能.

关键词:

继续进行预训练.自然语言处理自然语言处理.部分分类,部分分类.文字分类文本分类文本分类转移学习转移学习

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相关实验视频

Last Updated: Jul 16, 2025

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科学领域:

自然语言处理 (NLP) 是一种自然语言处理.
医疗保健中的机器学习
临床信息学临床信息学

背景情况:

临床笔记部分的准确分类 (例如主观,目标,评估,计划 - SOAP) 对于下游NLP任务至关重要,例如健康的社会决定因素和时间信息提取.
临床笔记部分分类模型往往在不同的医疗机构中表现出很差的可转移性,导致显著的准确性降低.
现有的方法在跨域概括方面扎,即使是使用先进的神经网络架构.

研究的目的:

开发和评估在SOAP框架内对临床笔记部分进行分类的方法,这些方法可以在各机构之间改善可转移性.
调查持续预训练策略对临床注释部分分类模型跨领域性能的影响.

主要方法:

基线模型使用基于BERT的架构进行了微调.
持续预训练技术,包括领域适应性预训练和任务适应性预训练,用于增强模型可转移性.
在微调过程中将域内注释样本纳入的效果在不同的样本大小中进行了分析.
从继续预训练中获得的绩效改善被量化为相当于域内注释样本的数量.

主要成果:

单独继续预训练并没有显著改善模型的性能.
当与少量域内注释样本相结合时,持续预训练使F1得分从0.756提高到0.808 (在3个数据集中的平均值).
发现这种性能提升相当于增加大约35个域内注释样本.

结论:

持续预培训是提高临床注释部分分类模型可转移性的有效策略,特别是在跨领域的场景中.
持续预训练的好处是最明显的,即使增加了有限量的域内标记数据.
这种方法提供了一种可行的方法,可以提高NLP模型在医疗保健环境中的通用性,而不需要广泛的域内数据注释.