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Improving Translational Accuracy02:07

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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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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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Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
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Updated: Sep 12, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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用大型语言模型优化精神病治疗数据中的实体识别.

Seyed Mohammad Bagher Hosseini1, Mohammad Javad Momeni Nezhad1, Mahdis Hosseini1

  • 1Columbia University Irving Medical Center, New York, NY.

Studies in health technology and informatics
|August 8, 2025
PubMed
概括

小语言模型 (LLM) 可以有效地从患者信息中提取药物不良反应 (ADR),即使有严格的数据隐私规则. 这项技术有助于实时监控,并提高了患者的安全.

关键词:
药物不良反应 药物不良反应实体提取 实体提取大型语言模型.名称 实体识别 名称 实体识别患者的信息 患者信息

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科学领域:

  • 自然语言处理自然语言处理.
  • 药物监督 药物监督 药物监督
  • 医疗保健中的人工智能

背景情况:

  • 从患者自我报告的信息中提取药物不良反应 (ADR) 对药物监测至关重要.
  • HIPAA的限制和数据隐私挑战使得从敏感的患者数据中提取ADR复杂化.
  • 对于实时ADR监控的高效和保护隐私的方法的需求正在增加.

研究的目的:

  • 评估局部部署的小语言模型 (LLM) 的有效性,以从患者自我报告的信息中提取药物不良反应 (ADR).
  • 评估Mistral-7B,Llama-3-8B和Gemma-7B在数据隐私约束下的ADR提取中的LLM的性能.
  • 探索在上下文学习,演示选择和微调使用QLoRA来优化ADR提取.

主要方法:

  • 利用PsyTAR数据集,包括患者自我报告的信息.
  • 实施并比较了三个小型LLM:米斯特拉-7B,拉玛-3-8B和杰玛-7B.
  • 应用技术,包括上下文学习,演示选择和QLoRA的微调.

主要成果:

  • 米斯特拉-7B在少数射击学习场景中表现出卓越的性能.
  • 微调实现了ADR提取的高F1得分86%.
  • 开发的管道可以实时监控ADR,同时保持数据隐私.

结论:

  • 即使在严格的数据隐私法规下,小型的,可在本地部署的LLM对于ADR提取是有效的.
  • 这些资源高效的解决方案使医疗机构能够通过快速ADR识别来提高患者安全.
  • 这些发现支持使用可访问的人工智能技术进行实时药物监测并改善患者的治疗结果.