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相关概念视频

Opioid Analgesics: Synthetic and Semisynthetic Opioids01:15

Opioid Analgesics: Synthetic and Semisynthetic Opioids

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Synthetic and semisynthetic opioids are pivotal in pain management and tackling opioid addiction. Semisynthetic opioids, including morphinans (morphine derivatives), oxycodone, oxymorphone, hydrocodone, and hydromorphone, have improved pharmacokinetic profiles compared to morphine. Additionally, heroin and 6-MAM (6-Monoacetylmorphine) show better CNS penetration than morphine due to heightened lipid solubility. Hydromorphone, a potent opioid, undergoes hepatic metabolism to form the active...
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Opioid Receptors: Overview01:22

Opioid Receptors: Overview

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Opioid receptors, including the mu (μ, MOR), delta (δ, DOR), and kappa (κ, KOR) types, belong to the rhodopsin family of G protein-coupled receptors. These receptors are located throughout the central and peripheral nervous systems and in non-neuronal tissues such as macrophages and astrocytes. Opioid receptor ligands can be categorized into agonists or antagonists. Highly selective agonists include [d-Ala2, MePhe4, Gly(ol)5]-enkephalin or DAMGO for MOR, [D-Pen2,...
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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可解释的机器学习来预测与阿片类药物相关的异常行为:使用临床文本和结构化数据的多模式方法.

Mubashir Farooq1, Asif Ali Banka1

  • 1Department of Computer Science and Engineering, Islamic University of Science and Technology, Kashmir, Jammu and Kashmir 192122, India.

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概括

这项研究开发了一种人工智能模型,以预测早期的阿片类药物滥用. 可解释的AI框架准确地识别了患有阿片类药物相关异常行为风险的患者,有助于安全的阿片类药物管理.

关键词:
成行为成行为临床文字分析 临床文字分析可解释的人工智能多模式学习是多模式学习.片类药物异常行为可以解释SHAP的解释性

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科学领域:

  • 医疗保健中的人工智能
  • 机器学习用于临床决策支持
  • 阿片类药物使用障碍研究研究

背景情况:

  • 阿片类药物流行仍然是一个关键的公共卫生问题,慢性疼痛治疗和过量死亡率的上升加剧了这一问题.
  • 与阿片类药物相关的异常行为 (ORAB) 是潜在阿片类药物滥用的早期指标,需要先进的预测工具.
  • 当前的风险评估模型往往缺乏有效临床实施所需的准确性和可解释性.

研究的目的:

  • 开发和验证一个可解释的机器学习框架,用于预测已确认的ORAB.
  • 整合各种数据源,包括临床文本和结构化电子健康记录 (EHR) 数据,以提高预测准确度.
  • 提供临床可操作的洞察力,了解导致ORABs的因素.

主要方法:

  • 结合GloVe和ClinicalBERT嵌入式用于EHR文本分析的多式模式方法.
  • 实施合成少数群体过量采样技术 (SMOTE) 来解决数据不平衡问题.
  • 训练和评估各种机器学习算法,包括集合方法和神经网络,使用SHAP进行解释.

主要成果:

  • 片风险组合模型实现了96.0%的AUROC和98.8%的准确性.
  • 使用ClinicalBERT的阿片类风险神经网络显示了98.75%的AUROC和98.47%的准确性.
  • SHAP分析确定了阿片类药物和类二类药物处方,以及与中枢神经系统相关的因素,作为重要的预测因素,并强调了关键的临床注释术语.

结论:

  • 开发的可解释人工智能框架为现代医疗保健决策提供了有价值的工具.
  • 这种方法提高了预测ORAB的能力,支持更安全的阿片类药物管理策略.
  • 多模式,可解释的人工智能对改善患者安全和减轻阿片类药物流行病的影响具有重大承诺.