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相关概念视频

Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Improving Translational Accuracy02:07

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Modeling in Therapy01:26

Modeling in Therapy

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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
Participant modeling involves therapists demonstrating calm and effective behaviors in...
61

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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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CALLM:通过使用大型语言模型进行数据增强来增强临床访谈分析.

Yuqi Wu, Kaining Mao, Yanbo Zhang

    IEEE journal of biomedical and health informatics
    |July 29, 2024
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    概括
    此摘要是机器生成的。

    这项研究引入了一个新的框架,使用大语言模型 (LLM) 来创建用于心理健康诊断的合成临床数据. 这种具有成本效益的方法可以提高机器学习模型的性能,特别是在零射击和少数射击的学习场景中.

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    科学领域:

    • 医疗保健中的人工智能
    • 计算精神病学是一种计算精神病学.
    • 机器学习用于心理健康

    背景情况:

    • 全球精神健康障碍的增加造成了重大的经济负担.
    • 临床数据的稀缺和不平衡挑战了自动心理健康诊断.
    • 现有的机器学习模型与有限的,不均的数据集作斗争.

    研究的目的:

    • 引入使用大型语言模型 (CALLM) 的新型临床成绩单数据增强框架.
    • 生成现实的合成临床数据,以解决数据稀缺和不平衡的问题.
    • 提高机器学习算法在自动心理健康诊断中的有效性.

    主要方法:

    • 开发了一个CALLM框架,使用"患者-医生角色扮演"来生成数据.
    • 引入了一个"教科书-分配-应用程序" (T-A-A) 分区,用于系统的数据集创建.
    • 使用"响应-理由"提示工程范式来生成真实的转录.

    主要成果:

    • 在E-DAIC PTSD数据集上使用微调的DistilBERT模型实现了0.77平衡精度,0.70F1得分和0.78AUC.
    • 在零射击学习 (ZSL) 和少数射击学习 (FSL) 场景中表现出强大的适应能力.
    • 与其他增强方法和PTSD诊断工作相比,CALLM框架显示了持续的改进,其成本低于传统数据收集成本的1%.

    结论:

    • 该CALLM框架有效地产生高质量的合成临床数据用于心理健康诊断.
    • 这种方法显著提高了机器学习模型的性能,特别是在低数据的制度.
    • 卡尔姆提供了一种具有成本效益和可扩展的解决方案,用于增强临床数据集,推进自动心理健康诊断.