在老年人医疗保健研究中使用机器学习进行预测建模的新视野
1Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Age and ageing
|September 23, 2024
概括
本指南为卫生研究人员介绍机器学习 (ML),涵盖预测模型开发,评估和报告. 它强调数据质量和验证,以在大数据时代为临床决策支持.
科学领域:
- * 医疗信息学 医疗信息学
- * 计算生物学 * 计算生物学
- * 临床流行病学 临床流行病学
背景情况:
- * 机器学习 (ML) 和预测建模在医疗保健中越来越重要,用于临床决策支持,特别是大数据.
- * 精准医学严重依赖于准确的预测模型.
- * 卫生研究人员需要关于临床环境中ML应用的可访问指导.
研究的目的:
- * 为健康研究人员和对预测建模感兴趣的读者提供机器学习的入门指南.
- *涵盖医疗保健中开发,评估和报告ML模型的所有方面.
- * 让读者能够批判性地评估和开发他们自己的ML模型.
主要方法:
- *不同预测和机器学习方法 (监督,无监督,半监督) 的概述.
- * 介绍关键的理论ML概念和流行的算法.
- *强调数据质量,预处理和公正的绩效评估.
主要成果:
- *讨论模型验证概念 (表面,内部,外部) 和性能指标 (歧视,校准).
- * 探索模型解释,公平性和临床实施的挑战.
- * 在预测建模和机器学习研究中识别常见的陷.
结论:
- * 这篇论文旨在增强对研究中的ML模型的理解和批判性评估.
- * 它作为开发,评估和在临床实践中实施ML模型的基础指南.
- * 适当的报告和陷的意识对于医疗保健中可靠的ML应用至关重要.
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