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相关概念视频

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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During the development of a new pharmaceutical, the manufacturer initially assigns a code name to the drug. Once approved, the drug receives a United States Adopted Name (USAN)—a generic, nonproprietary designation. Upon being listed in the United States Pharmacopeia, this nonproprietary name becomes the drug's official name. Additionally, the manufacturer assigns a proprietary name or trademark, which serves as the brand name under which the drug is marketed. It is worth noting that...
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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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用大型语言模型提高临床笔记中的物质使用检测.

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    大型语言模型 (LLM) 可以有效地在电子健康记录 (EHR) 中识别物质使用行为. 一个微调的LLM在检测各种物质使用类别,包括阿片类药物滥用等方面取得了很高的准确性.

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    科学领域:

    • 医疗信息学 医疗信息学
    • 自然语言处理自然语言处理.
    • 公共卫生监督 公共卫生监督

    背景情况:

    • 在电子健康记录 (EHR) 中识别物质使用是很困难的,因为非结构化的临床笔记,各种术语和否定.
    • 准确的检测对于患者护理,临床决策支持和对物质使用行为的公共卫生监测至关重要.

    研究的目的:

    • 开发和评估大型语言模型 (LLM) 以检测EHR排放摘要中的八种物质使用类别.
    • 为药物检测创建一个大型的注释数据集,以支持系统性物质使用监测.

    主要方法:

    • 使用MIMIC-III/IV排放摘要构建一个注释的药物检测数据集.
    • 研究了多个LLM在零射击,少数射击和微调设置中的性能.
    • 评估了检测个人物质使用,处方阿片类药物滥用和多重物质使用的模型.

    主要成果:

    • 一个微调的LLM,Llama-DrugDetector-70B,表现出卓越的性能.
    • 在大多数个体物质使用类别中获得了近乎完美的F1分数 (>=0.95).
    • 在处方阿片类药物滥用 (F1=0.815) 和多重物质使用 (F1=0.917) 方面表现强.

    结论:

    • LLM显著提高了在EHR中检测物质使用行为的能力.
    • 精心调整的LLM在临床决策支持和物质使用监测研究方面表现有前途.
    • 需要进一步的研究来解决LLM应用程序在这个领域的可扩展性.