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相关概念视频

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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, controlled...

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了解基于人工智能的临床预测模型对死亡率的负责任发展:范围审查协议.

Riley Martens1, Jessalyn K Holodinsky1,2,3,4, Jessica Simon1,5,6

  • 1Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3280 Hospital Dr NW, Calgary, AB, T2N 5A1, Canada, 1 (403) 220 6940.

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概括

基于人工智能的临床预测模型 (AIPMs) 可以改善死亡率预测,但有可能恶化健康不平等. 本综述综合了关于AIPM开发和应用的文献,以促进医疗保健中的负责任创新.

关键词:
在这里,我们可以看到AIAIAI.RRI RRI 在这里.人工智能的人工智能是人工智能.知识综合 知识综合机器伦理学 机器伦理学死亡率预测死亡率预测患者参与 患者参与负责发展 负责任的发展负责任的研究和创新社会的技术和社会技术.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 临床决策支持 临床决策支持

背景情况:

  • 预后不平等为代表性不足的群体创造了终身护理的障碍.
  • 基于人工智能的临床预测模型 (AIPMs) 提供了可访问的死亡率预测的潜力,但可能会加剧现有的健康不平等性,原因是偏见的数据和不透明.
  • 伦理考虑对于负责任的AIPM开发和部署至关重要.

研究的目的:

  • 综合同行评审的关于AIPM的创建和应用在成人急性护理环境中的死亡率预测的文献.
  • 为AIPMs提供对负责任和道德模型开发的见解.
  • 确定AIPM开发中的关键元素,用于死亡率预测.

主要方法:

  • 在多个学术数据库 (Medline,Embase,IEEE Xplore,ACM数字图书馆,Compendex,Scopus) 中采用了一个跨学科的搜索策略.
  • 文学选包括两个轮 (标题/摘要,然后是全文) 具体的资格标准.
  • 数据分析将利用由负责任的研究和创新 (RRI) 框架提供信息的描述性,摘要性和定性综合.

主要成果:

  • 文献搜索于2025年3月25日完成,并于2025年5月开始选.
  • 预计结果将于2026年1月公布.
  • 本次审查将详细介绍AIPM用于死亡率预测的开发中所包含的具体元素.

结论:

  • 这次审查将提供AIPM用于死亡率预测的全面摘要.
  • 该研究将通过负责任的研究和创新 (RRI) 框架的透视来分析AIPM的发展,重点是预测,反射性,包容性和响应性.
  • 重点将放在跨学科合作,计算和临床伦理学以及利益相关方参与负责任的创新上.