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相关概念视频

Pharmacovigilance01:19

Pharmacovigilance

1.6K
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.
In some cases, there...
1.6K
Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

1.7K
Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
1.7K
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

334
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
334
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

657
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
657
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

382
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
382
Measurement of Bioavailability: Pharmacodynamic Methods01:20

Measurement of Bioavailability: Pharmacodynamic Methods

217
Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
217

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相关实验视频

Updated: Jan 9, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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预测药物不良事件的机器学习方法:系统性审查

Niaz Chalabianloo1,2,3, Fatemeh Ahmadi3,4, Mohammad Ali Omrani1

  • 1Department of Physiology and Pharmacology, Western University, London, Ontario, Canada.

British journal of clinical pharmacology
|December 5, 2025
PubMed
概括

机器学习模型显示,在门诊环境中预测药物不良事件 (ADEs) 是有前途的. 然而,数据不平衡和有限的外部验证带来的挑战需要进一步的研究,以获得可靠的临床使用.

关键词:
药物不良事件是药物不良事件.机器学习是机器学习.门诊患者的治疗方式药物监督和药物监督预测建模预测建模系统性审查 系统性审查

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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相关实验视频

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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科学领域:

  • 药物监督 药物监督 药物监督
  • 医疗信息学 医疗信息学
  • 医疗保健中的机器学习

背景情况:

  • 预测药物不良事件 (ADEs) 对患者安全和门诊护理成本降低至关重要.
  • 传统方法面临复杂的医疗保健数据的局限性,促使机器学习 (ML) 的探索.
  • 在现实世界门诊环境中,ML模型对ADE预测的有效性需要系统评估.

研究的目的:

  • 系统地审查机器学习算法,用于在门诊环境中预测药物不良事件.
  • 分析研究特征,ML方法,性能指标和现有研究中的偏差风险.
  • 确定基于 ML 的 ADE 预测的当前局限性和未来方向.

主要方法:

  • 在MEDLINE和Embase的系统文献搜索到2024年12月.
  • 在门诊或类似的大规模数据中包含开发或验证用于ADE预测的ML模型的研究.
  • 使用PROBAST工具评估研究特征,ML算法,性能 (AUC) 和偏差风险.

主要成果:

  • 在59项研究中分析了191个ML实现;物流回归,随机森林和XGBoost是常见的.
  • 大多数研究 (85%) 报告了中度至高的内部验证性能 (AUC > 0.70).
  • 存在显著的方法差距,包括处理不良的阶级失衡 (33.9%) 和有限的外部验证 (18.6%).

结论:

  • 机器学习模型,特别是组合方法,显示出预测门诊药物不良事件的潜力.
  • 目前解决阶级不平衡和进行外部验证的局限性阻碍了广泛的临床采用.
  • 未来的研究必须优先考虑严格的方法,外部验证和与药物监督实践的整合,以可靠地预测ADE.