临床数据挖掘:对翻译应用的挑战,机遇和建议
Huimin Qiao1, Yijing Chen2, Changshun Qian3
1Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Journal of translational medicine
|February 20, 2024
概括
临床数据挖掘有助于风险分层和诊断,但面临着翻译方面的挑战. 一个新的框架 (CSCF) 指导开发准确医学的验证预测模型.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据挖掘 临床数据挖掘
- 预测建模预测建模
背景情况:
- 临床数据挖掘利用现实世界和实验数据来完成诸如风险分层和生存预测等任务.
- 由于临床要求和数据挖掘实践的不一致,这些模型的翻译应用是有限的.
- 来自数据挖掘的异国情调的预测往往是当地医疗机构很难实现的.
研究的目的:
- 审查临床数据挖掘的翻译应用.
- 确定开发和验证预测模型的挑战.
- 提出一个框架,以提高数据挖掘模型的临床实用性.
主要方法:
- 临床数据挖掘原则和实践的系统审查.
- 对从临床实践中脱离和滥用模型验证的原因的分析.
- 临床上下文,以子组为导向,混和假阳性控制 (CSCF) 框架的建议.
主要成果:
- 确定了与数据挖掘方法同步临床需求的关键挑战.
- 在模型验证和在临床环境中直接应用的突出问题.
- 引入了CSCF框架,以指导临床相关预测模型的开发.
结论:
- 该CSCF框架为在临床环境中开发和验证预测模型提供了指导.
- 解决翻译差距对于实现精准医学的潜力至关重要.
- 未来的研究应该专注于个性化的预测模型,以确定具有差异治疗益处或风险的患者子组.
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