在个体患者层面预测结果:什么是最好的方法?
Qiang Liu1,2,3, Edoardo Giuseppe Ostinelli4,2,5, Franco De Crescenzo4,2
1Department of Psychiatry, University of Oxford, Oxford, UK qiang.liu@bristol.ac.uk.
BMJ mental health
|June 14, 2023
概括
对抑郁症预测模型的端对端和基于相似性的方法显示了可比的性能. 对于治疗预测中的临床和人口统计数据,建议采用更简单的端到端方法.
科学领域:
- *计算精神病学和医疗保健中的机器学习.
- * 开发和验证心理健康状况的预测模型.
背景情况:
- *患者异质性对开发准确的抑郁症预测模型构成挑战.
- * 两种常见的方法包括端到端模型 (使用所有数据) 和基于相似性的模型 (首先对患者进行集群).
- *这些方法在预测抑郁症结果方面的比较性能仍然不清楚.
研究的目的:
- * 实证地比较端对端和基于相似性的抑郁症方法的预测性能.
- *为了说明基于相似性的方法在现实世界的临床数据集中的应用.
主要方法:
- *使用了来自16384名开始抗抑郁药治疗的英国初级保健数据.
- * 用于基于相似性的方法和用于两种方法的模型开发的脊回归的k-means集群.
- *使用平均绝对误差 (MAE) 和确定系数 (R2) 评估模型性能.
主要成果:
- *端到端的模型实现了MAE为4.64和R2为0.20.
- * 基于相似性的最佳模型 (四个集群) 的MAE为4.65,R2为0.19.
- * 两种方法都显示了可比的预测性能.
结论:
- * 预测抑郁症严重程度的端对端和基于相似性的模型表现相似.
- * 由于其在使用人口统计和临床数据进行治疗预测模型时的简单性,因此偏爱端到端的方法.
- *进一步的研究可能会探索其他聚类或建模技术,以潜在地增强基于相似性的方法.
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