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Updated: Jan 8, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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基于变压器的ADR检测模型:与大型语言模型进行交叉药物验证和比较

Minjung Kim1, Kyoung Eun Kim1, Jae-Hee Kwon1

  • 1College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul, Republic of Korea.

Therapeutic advances in drug safety
|December 22, 2025
PubMed
概括

大型语言模型 (LLM) 在从社交媒体数据中检测药物不良反应 (ADR) 方面明显优于基于变压器的模型. 聊天GPT 4o-mini在关于GLP-1受体激动剂的推文中在识别ADR方面表现出卓越的表现.

关键词:
贝尔特 (BERT) 公司作为GLP-1受体的激动剂.在 GPT 中,GPT 必须是 GPT.药物不良反应 药物不良反应社交媒体 社交媒体转移学习转移学习

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Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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