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相关概念视频

Drug Discovery: Overview01:26

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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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Drug regulation encompasses the management of drug usage by evaluating its safety and efficacy through assessments conducted by regulatory authorities. Regrettably, the history of drug regulation is marred by several catastrophic events. One such incident is the Elixir Sulfanilamide tragedy, in which the toxic compound diethyl glycol was included in a sweet-tasting medication, leading to numerous fatalities. This event prompted the enactment of the Food, Drug, and Cosmetic Act in 1938. Under...
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Drug-receptor bonds are formed through various chemical forces when drugs interact with target cells. Covalent bonds, strong and irreversible, are exemplified by DNA-alkylating anticancer agents that inhibit cell division. However, such irreversible drug binding lacks selectivity and can modify the DNA of the surrounding healthy cells. Covalent binding often contributes to tissue toxicity, as seen with chloroform and paracetamol metabolites binding to the liver, causing hepatotoxicity.
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When it comes to infants and young children, they are typically administered smaller doses of medication in comparison to adults. This is primarily because their organ functions still need to fully develop, meaning their bodies are not as efficient at metabolizing or eliminating drugs. Additionally, their blood-brain barrier is more permeable than in adults. As a result, high concentrations of drugs can easily penetrate the central nervous system (CNS), potentially leading to neurological...
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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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Medications are typically administered to achieve therapeutic effects. Some drugs can modify an individual's mood and perception, frequently resulting in various enjoyable experiences. However, this can result in drug dependency, a condition marked by continuous drug use despite potential negative consequences. Drug dependency primarily falls into two categories: psychological and physical dependence. Psychological dependence occurs when the pleasurable feelings induced by the drug...
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基于深度学习的方法,用于对患者药物评价的情绪分析.

Sena Al-Hadhrami1, Tamas Vinko1, Tawfik Al-Hadhrami2

  • 1Institute of Informatics, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary.

PeerJ. Computer science
|May 3, 2024
PubMed
概括

深度学习模型,包括双向LSTM和Bi-LSTM-CNN,有效地分析患者药物评价的情绪. 在这种情绪分析任务中,GloVe的词嵌入显著提高了模型的性能.

关键词:
两种LSTM-CNN的CNN.双向LSTM-CNN双向LSTM-CNN双向LSTM-CNN双向LSTM-CNN双向LSTM-CNN双向LSTM 双向LSTM-CNN双向LSTM在美国,CNN是CNN.深度学习是一种深度学习.这是LSTM的LSTM.患者的药物评价 患者的药物评价情绪分析是一种情绪分析.

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科学领域:

  • 自然语言处理自然语言处理.
  • 人工智能的人工智能
  • 药物监督 药物监督 药物监督

背景情况:

  • 患者药物评价为药物的有效性和副作用提供了宝贵的见解.
  • 在这些评论中分析情绪对于理解患者体验和改善药物安全至关重要.
  • 传统的方法经常与非结构化文本数据的细微差别和复杂性作斗争.

研究的目的:

  • 评估深度学习模型,用于对患者药物评价的情感分析.
  • 为了比较双向长期短期记忆 (LSTM) 和混合双向LSTM-CNN模型的性能.
  • 评估GloVe词嵌入对情感分类准确性的影响.

主要方法:

  • 实施和评估双向LSTM和双向LSTM-CNN模型.
  • 使用GloVe词嵌入用于特征表示.
  • 在两个不同的药物审查数据集上进行培训和测试模型.
  • 基于评论文本,医疗状况和评分分数的情绪分类.

主要成果:

  • Bi-LSTM-CNN模型实现了96%的准确性 (模型A).
  • 该Bi-LSTM模型实现了87%的准确性 (模型B).
  • 全球词嵌入显然改善了模型性能,由科恩的卡帕系数证实.

结论:

  • 深度学习方法,特别是双向LSTM和Bi-LSTM-CNN,对于对患者药物评价的情绪分析非常有效.
  • 整合GloVe词嵌入增强了情绪分类的准确性和可靠性.
  • 这些发现支持使用先进的NLP技术从患者生成的健康数据中提取有意义的见解.